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Introducing HyperTune V2! New Features Include: - Partnership with AMD & Intel - Automatic Overclocking (CPU & GPU) - Game Profiles (CS2, Valorant, Rust, and more!) - Network Optimizations - RAM Optimizations

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HYPERTUNE

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118,933 просмотров • 2 месяцев назад •via X (Twitter)

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$AMD Strategic Price Positioning Long🧵 AMD is increasingly the most hated semi stock that can rival $NVDA dominance in GPUs and software(Cuda v. ROCm). $AMD is also the most under-owned among all Funds in 2025 according to Bank of America! For what I learnt for years as an investor with Dr. Lisa Su, all analysts and market are underestimate Dr. Su leadership. $AMD is capable of raising price, making high quality hardware with software. Dr. Su or AMD choice to adopt a lower price strategy to gain market share is a deliberate and multifacets approach rooted in competitive positioning, market dynamics, and long-term growth objectives. As an investor, it may take time like CPUs and embedded to see margin improving. 1. . Penetration Pricing to Challenge Dominant Competitors AMD has historically positioned itself as a cost-effective alternative to dominant players like Intel in CPUs and Nvidia in GPUs. By setting prices lower than competitors, AMD aims to attract customers and quickly gain market share. This is a classic penetration pricing strategy, where the goal is to capture a significant portion of the market by offering high-performance products at a lower price point. ~CPU Market Example: When AMD launched its Ryzen processors in 2017, it priced them competitively compared to Intel's Core processors, emphasizing a better price-to-performance ratio. Ryzen CPUs offered higher core counts and multi-core performance at lower prices, appealing to cost-conscious consumers, gamers, and professionals. This strategy helped AMD increase its CPU market share to 16.6% by early 2025, narrowing the gap with Intel. ~GPU Market Context: In the GPU market, where Nvidia holds an 88% share compared to AMD's 12%, AMD has been criticized for not launching GPUs at low enough prices to compete effectively. However, posts on X and articles suggest AMD is shifting its GPU strategy to focus on mainstream, cost-effective products rather than high-end enthusiast segments, aiming to regain market share through competitive pricing. 2. Appealing to Cost-Conscious Market Segments AMD targets price-sensitive customers, including gamers, small businesses, and enterprises looking for high-performance computing at a lower cost. This is particularly effective in segments where performance is critical, but budgets are constrained. ~Value Proposition: AMD’s Ryzen and EPYC processors, as well as Radeon GPUs, are designed to deliver performance comparable to or better than competitors in specific workloads (e.g., multi-core processing or AI compute) at a lower price. For example, Ryzen processors have been noted for their superior multi-core performance compared to Intel CPUs at similar or lower price points, making them attractive for tasks like video editing or gaming. ~AI and Data Center: In the AI and data center markets, AMD’s cost-effective Instinct MI300X GPUs and EPYC CPUs target enterprises seeking affordable alternatives to Nvidia’s expensive AI ecosystem. This strategy taps into an underleveraged market segment that Nvidia’s broad, premium-priced AI solutions may not fully address. 3. Building Scale and Developer Support AMD’s leadership, including Jack Huynh, has emphasized the importance of scale—gaining a larger market share to attract developer support and optimize software ecosystems. A lower price strategy helps AMD achieve this by increasing adoption among consumers and enterprises. ~Gaming GPUs: By focusing on mainstream GPUs with competitive pricing (e.g., targeting an 80% addressable market rather than the high-end 10%), AMD aims to build a larger user base. This scale encourages developers to optimize games for AMD’s technologies, such as FSR 3 (FidelityFX Super Resolution) and Anti-Lag 2, improving the ecosystem and competitiveness against Nvidia’s CUDA platform. ~Open Ecosystem in AI: AMD’s open-source ROCm platform contrasts with Nvidia’s proprietary CUDA, appealing to developers who prefer flexibility. Lower-priced hardware makes it easier for developers to adopt AMD’s solutions, fostering a broader AI software ecosystem. 4. Historical Context and Brand Positioning Since its founding in 1969, AMD has positioned itself as a challenger brand, often acting as a “second source” supplier to Intel. This role required competitive pricing to gain a foothold in markets dominated by established players. Over time, AMD has built a reputation for quality and affordability, reinforced by products like the Am9080 (a reverse-engineered Intel 8080) and modern Ryzen and EPYC lines. This historical strategy of undercutting competitors’ prices while delivering comparable performance continues to define AMD’s approach. 5. Countering Competitor Dominance AMD operates in highly competitive markets where Intel and Nvidia have significant advantages in brand recognition, market share, and ecosystems. A lower price strategy is a pragmatic way to disrupt this in CPUs: ~Intel’s historical dominance in the CPU market (servers, desktops, and laptops) has been challenged by AMD’s Ryzen and EPYC processors, which offer better value. For instance, AMD’s EPYC CPUs have driven a 122% year-over-year revenue increase in the data center segment, partly due to their cost-effectiveness, helping AMD capture 94% of CPU sales at some retailers. ~Nvidia in GPUs: Nvidia’s 88% GPU market share and premium pricing (e.g., high-end GPUs like the RTX 4090) leave room for AMD to compete in the mid-to-low range. However, AMD’s failure to launch GPUs at sufficiently low prices (e.g., the RX 7900 XT at $900 instead of its current $680) has limited its success, prompting a strategic shift toward more aggressive pricing in future RDNA 4 GPUs. 6. Market Share as a Long-Term Investment AMD’s lower price strategy is not just about immediate sales but also about long-term market positioning. 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$AMD Strategic Price Positioning Long🧵 AMD is increasingly the most hated semi stock that can rival $NVDA dominance in GPUs and software(Cuda v. ROCm). $AMD is also the most under-owned among all Funds in 2025 according to Bank of America! For what I learnt for years as an investor with Dr. Lisa Su, all analysts and market are underestimate Dr. Su leadership. $AMD is capable of raising price, making high quality hardware with software. Dr. Su or AMD choice to adopt a lower price strategy to gain market share is a deliberate and multifacets approach rooted in competitive positioning, market dynamics, and long-term growth objectives. As an investor, it may take time like CPUs and embedded to see margin improving. 1. . Penetration Pricing to Challenge Dominant Competitors AMD has historically positioned itself as a cost-effective alternative to dominant players like Intel in CPUs and Nvidia in GPUs. By setting prices lower than competitors, AMD aims to attract customers and quickly gain market share. This is a classic penetration pricing strategy, where the goal is to capture a significant portion of the market by offering high-performance products at a lower price point. ~CPU Market Example: When AMD launched its Ryzen processors in 2017, it priced them competitively compared to Intel's Core processors, emphasizing a better price-to-performance ratio. Ryzen CPUs offered higher core counts and multi-core performance at lower prices, appealing to cost-conscious consumers, gamers, and professionals. This strategy helped AMD increase its CPU market share to 16.6% by early 2025, narrowing the gap with Intel. ~GPU Market Context: In the GPU market, where Nvidia holds an 88% share compared to AMD's 12%, AMD has been criticized for not launching GPUs at low enough prices to compete effectively. However, posts on X and articles suggest AMD is shifting its GPU strategy to focus on mainstream, cost-effective products rather than high-end enthusiast segments, aiming to regain market share through competitive pricing. 2. Appealing to Cost-Conscious Market Segments AMD targets price-sensitive customers, including gamers, small businesses, and enterprises looking for high-performance computing at a lower cost. This is particularly effective in segments where performance is critical, but budgets are constrained. ~Value Proposition: AMD’s Ryzen and EPYC processors, as well as Radeon GPUs, are designed to deliver performance comparable to or better than competitors in specific workloads (e.g., multi-core processing or AI compute) at a lower price. For example, Ryzen processors have been noted for their superior multi-core performance compared to Intel CPUs at similar or lower price points, making them attractive for tasks like video editing or gaming. ~AI and Data Center: In the AI and data center markets, AMD’s cost-effective Instinct MI300X GPUs and EPYC CPUs target enterprises seeking affordable alternatives to Nvidia’s expensive AI ecosystem. This strategy taps into an underleveraged market segment that Nvidia’s broad, premium-priced AI solutions may not fully address. 3. Building Scale and Developer Support AMD’s leadership, including Jack Huynh, has emphasized the importance of scale—gaining a larger market share to attract developer support and optimize software ecosystems. A lower price strategy helps AMD achieve this by increasing adoption among consumers and enterprises. ~Gaming GPUs: By focusing on mainstream GPUs with competitive pricing (e.g., targeting an 80% addressable market rather than the high-end 10%), AMD aims to build a larger user base. This scale encourages developers to optimize games for AMD’s technologies, such as FSR 3 (FidelityFX Super Resolution) and Anti-Lag 2, improving the ecosystem and competitiveness against Nvidia’s CUDA platform. ~Open Ecosystem in AI: AMD’s open-source ROCm platform contrasts with Nvidia’s proprietary CUDA, appealing to developers who prefer flexibility. Lower-priced hardware makes it easier for developers to adopt AMD’s solutions, fostering a broader AI software ecosystem. 4. Historical Context and Brand Positioning Since its founding in 1969, AMD has positioned itself as a challenger brand, often acting as a “second source” supplier to Intel. This role required competitive pricing to gain a foothold in markets dominated by established players. Over time, AMD has built a reputation for quality and affordability, reinforced by products like the Am9080 (a reverse-engineered Intel 8080) and modern Ryzen and EPYC lines. This historical strategy of undercutting competitors’ prices while delivering comparable performance continues to define AMD’s approach. 5. Countering Competitor Dominance AMD operates in highly competitive markets where Intel and Nvidia have significant advantages in brand recognition, market share, and ecosystems. A lower price strategy is a pragmatic way to disrupt this in CPUs: ~Intel’s historical dominance in the CPU market (servers, desktops, and laptops) has been challenged by AMD’s Ryzen and EPYC processors, which offer better value. 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By capturing market share, AMD can: ~Increase Brand Loyalty: Affordable, high-performance products build customer loyalty, especially among gamers and small businesses, creating a foundation for future sales. ~Drive Revenue Growth: Market share gains in CPUs (e.g., 16.6% in 2025) and data centers (e.g., $3.5 billion in Q3 revenue) translate into higher revenue, even if margins are initially lower. ~Influence Industry Standards: Greater market presence allows AMD to influence hardware and software standards, such as pushing for open-source AI frameworks or gaming optimizations, reducing reliance on competitors’ proprietary systems. 7. 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This shift aligns with AMD’s broader goal of achieving 40–50% market share by targeting the “80%” of the market that prioritizes affordability over premium features. Lastly, AMD’s lower price strategy is a calculated move to disrupt Intel and Nvidia’s dominance, capture market share, and build scale for long-term growth. By offering high-performance CPUs and GPUs at competitive prices, AMD appeals to cost-conscious consumers and enterprises, particularly in the CPU and AI markets, where it has seen significant gains (e.g., 16.6% CPU market share and $3.5 billion in data center revenue). Recent price increase on MI350 and MI355 and more on MI400 signaled #AI chip leadership and pricing power, which will result in significant top and bottom line growth.

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My stream has crashed every stream Ive done for the last year and a half. I dont know what to do and im about to quit. I have a job- just my hobby is being destroyed ): Here are more videos. Please help. -started with OBS and now a bit less crashes in SLOBS - Nothing in the logs / event viewer says anything is wrong. - Ive reseated my ram. - All gamebar/overlays are off. - no overheating (CPU 38°, Mobo 42°, GPU 53°) -6 month old thermal paste -3 month old bios - all updated firmware on everything/storage/mobo/ram - power hub USB - no plugins - HAGS off - nvidia audio devices off - max game fps set to 60 /playing at low quality - NO overclocking - meld wont work - anker surge protector+2 outlets I cant play games on my powerhouse of a PC, only Playstation is working to avoid lags. Still crashes though. Specs below

WATCHHOLLIE

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🀄 Introducing the Riichi Style HD—an automatic riichi mahjong table made specifically for the U.S. market! 🇺🇸 Its voltage and electrical specifications are designed for use in the United States, giving players a premium Japanese mahjong experience with added convenience and speed. Key features include: ✅ Fully automatic operation ✅ RFID score display system Each player’s score is displayed in real time, making it easier to track point differences and plan your strategy. ✅ Black point stick support The table can automatically calculate and display negative scores when a player falls below zero. ✅ Automatic tile dealing Each player’s starting hand is dealt automatically, dramatically increasing game speed and creating a smoother playing experience. Play faster. Track scores more easily. Enjoy riichi mahjong at the highest level. Riichi Style HD—built specifically for riichi mahjong players in the United States.
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🀄 Introducing the Riichi Style HD—an automatic riichi mahjong table made specifically for the U.S. market! 🇺🇸 Its voltage and electrical specifications are designed for use in the United States, giving players a premium Japanese mahjong experience with added convenience and speed. Key features include: ✅ Fully automatic operation ✅ RFID score display system Each player’s score is displayed in real time, making it easier to track point differences and plan your strategy. ✅ Black point stick support The table can automatically calculate and display negative scores when a player falls below zero. ✅ Automatic tile dealing Each player’s starting hand is dealt automatically, dramatically increasing game speed and creating a smoother playing experience. Play faster. Track scores more easily. Enjoy riichi mahjong at the highest level. Riichi Style HD—built specifically for riichi mahjong players in the United States.

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Introducing the future of Prism and GameGPT. For the next 3 days, we’ll share an updated view on our product vision for 2024. With so much insider info coming, you won’t want to miss it! Let’s get started: To start, as you can see in the video, we’ve been hard at work quietly building the Prism Flywheel. The drops won’t stop this year with so much in progress: - New site (check out at for new links) - New Challenge Integrations (Valorant / CS2 are next) - New weekly drops in the Arcade - First co-developed partnership game launching VERY soon - Web3 game studios integrating their games into our Arcade - Genesis NFT Mint - Genesis NFT Cinematic - The Much Awaited GameGPT Product Demo - GameGPT Closed Alpha - GameGPT Creator’s Program season 0 - Prism Mobile App - New Blockchain integrations - And MUCH MORE We hope you’re ready for a jam-packed 2024. Look for our post tomorrow as we drop more info including an updated roadmap (with dates) for all of the events listed above.

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436,838 просмотров • 2 лет назад

$AMD $MSFT Partnership is MASSIVE in 2026 🚀 If you were excited about my thread on $AMD $AMZN AWS long time partnership, you will be even more excited about what Microsoft gonna do with 2026 AMD EPYC "Venice". Historical Context: The relationship between AMD and Microsoft began in the early 2000s, with Microsoft initially focusing on Intel's x86 architecture for its Windows operating system and server products. However, AMD's entry into the server market with its Opteron processors in 2003 marked the beginning of a competitive dynamic that eventually led to collaboration. The partnership intensified with the launch of 3rd Generation EPYC "Milan" in 2021, powering Azure's N2D and C2D VM families. By 2025, Microsoft had integrated 5th Generation EPYC "Turin" into new compute-optimized instances, reflecting a strategic shift towards AMD for cost and performance benefits. This "Secret Weapon" breakthrough will mark another inflection point for AMD Microsoft Azure relationship, will probably be more aggressive than EPYC "Milan" moment in 2021. We can call it EPYC "Venice" moment 2026" 1. Technical performance of AMD EPYC "Venice" (2026) AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for Microsoft Azure , enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. ~Up to 256 Zen 6 cores, a 70% performance increase over "Turin," optimized for AI and HPC. ~Memory and Bandwidth: 1.6 TB/s per socket, doubling "Turin's" capability, with support for MR-DIMM/MCR-DIMM. ~Efficiency: 1,500-1,700W power draw, a 50% reduction, aligning with Microsoft's sustainability initiatives. ~Interconnect: PCIe 6.0 and a new chiplet fabric for rack-scale AI, reducing latency and enhancing scalability. 2. Why $MSFT will adopt $AMD YPYC Share to 50%+ in 2026. AMD EPYC Share: ~30-35% of Azure's x86 CPU-based business while Intel Xeon share is 65% Microsoft's Azure has been progressively integrating AMD EPYC, with "Venice" expected to expand this footprint: A. Dominance of AI Inference Workloads ~AI inference constitutes 80% of AI workloads in cloud environments, with latency-sensitive applications like chatbots, recommendation engines, and fraud detection requiring sub-second response times. ~"Venice's" 35x inference performance uplift directly addresses these requirements, outperforming Intel's offerings and custom Arm solutions in multi-threaded scenarios. B. Cost Efficiency and Operational Savings ~Azure's 2025 capex of $118B is under pressure to deliver returns. "Venice" can reduce operational expenses by $20-30B annually due to its power efficiency and performance gains, improving Azure's margins to 35-40%. ~The cost per inference operation is significantly lower with "Venice," estimated at 24-31% less than Intel-based alternatives, enhancing Azure's competitiveness against AWS and GCP. C. Scalability for Enterprise AI: ~"Venice" supports rack-scale AI deployments, enabling Azure to scale AI services for enterprise customers. For example, a 1,000-node cluster can process 700,000+ tokens per second, crucial for large-scale AI applications like personalized marketing and predictive analytics. ~This scalability is particularly important as Azure aims to capture the $100B+ AI opportunity by 2026, as stated by Microsoft CEO Satya Nadella. D. Reduction of Nvidia Dependency ~While Nvidia ( $NVDA) dominates AI accelerators, AMD's integrated EPYC-GPU solutions (MI450 with "Venice") offer a balanced approach, reducing Azure's reliance on Nvidia's high-cost GPUs. ~"Venice" enables hybrid inference models, where CPU-based inference handles 80% of workloads, and GPU acceleration is reserved for training and complex tasks, optimizing resource allocation. 3. Financial Implication: ~Revenue from Azure could reach $15-18B annually by 2026, part of a total revenue projection of $70-100B ~Profit margins could improve to 55-60%, boosting net income to $20-25B, supported by scale economies and reduced production costs. Intel could respond by giving more aggressive discounts, but this breakthrough has been a decade long of $AMD R&D, or rethinking chiplet design, a complete new approach. "Venice's" lead in AI inference and efficiency is challenging to match. Broader Industry: Other hyperscalers ( Amazon Web Services , GCP) and enterprises will follow Azure's lead, standardizing EPYC technology and pressuring Intel further. This could lead to a broader industry shift towards AMD, enhancing its ecosystem and bargaining power. Conclusion: The strategic adoption of AMD's 6th Generation EPYC "Venice" processors by Microsoft Azure in 2026 marks a pivotal moment in the evolution of cloud computing, particularly for AI inference capabilities. "Venice's" groundbreaking chiplet design, offering a 35x performance uplift for AI inference tasks, a 50% reduction in power consumption, and unparalleled scalability, positions Azure to leapfrog its competitors in the race for AI dominance. This technical superiority, combined with significant cost savings potentially $20-30B annually in operational expenses; aligns perfectly with Microsoft's ambitions to capture the $100B+ Revenue AI opportunity by 2026. The shift to 50% x86 market share for AMD within Azure is not merely a technical transition but a strategic realignment that redefines the competitive landscape. Historically, Microsoft's partnership with AMD has evolved from niche deployments to a core component of Azure's infrastructure, and "Venice" accelerates this trend. The 30-35% AMD EPYC share in 2025 is expected to double, driven by new VM families like C4D and H4D, which will dominate AI-intensive and HPC workloads. This migration is incentivized by "Venice's" efficiency gains, reducing dependency on Intel and Nvidia, and enhancing Azure's sustainability profile. Not Financial Advice!
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$AMD $MSFT Partnership is MASSIVE in 2026 🚀 If you were excited about my thread on $AMD $AMZN AWS long time partnership, you will be even more excited about what Microsoft gonna do with 2026 AMD EPYC "Venice". Historical Context: The relationship between AMD and Microsoft began in the early 2000s, with Microsoft initially focusing on Intel's x86 architecture for its Windows operating system and server products. However, AMD's entry into the server market with its Opteron processors in 2003 marked the beginning of a competitive dynamic that eventually led to collaboration. The partnership intensified with the launch of 3rd Generation EPYC "Milan" in 2021, powering Azure's N2D and C2D VM families. By 2025, Microsoft had integrated 5th Generation EPYC "Turin" into new compute-optimized instances, reflecting a strategic shift towards AMD for cost and performance benefits. This "Secret Weapon" breakthrough will mark another inflection point for AMD Microsoft Azure relationship, will probably be more aggressive than EPYC "Milan" moment in 2021. We can call it EPYC "Venice" moment 2026" 1. Technical performance of AMD EPYC "Venice" (2026) AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for Microsoft Azure , enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. ~Up to 256 Zen 6 cores, a 70% performance increase over "Turin," optimized for AI and HPC. ~Memory and Bandwidth: 1.6 TB/s per socket, doubling "Turin's" capability, with support for MR-DIMM/MCR-DIMM. ~Efficiency: 1,500-1,700W power draw, a 50% reduction, aligning with Microsoft's sustainability initiatives. ~Interconnect: PCIe 6.0 and a new chiplet fabric for rack-scale AI, reducing latency and enhancing scalability. 2. Why $MSFT will adopt $AMD YPYC Share to 50%+ in 2026. AMD EPYC Share: ~30-35% of Azure's x86 CPU-based business while Intel Xeon share is 65% Microsoft's Azure has been progressively integrating AMD EPYC, with "Venice" expected to expand this footprint: A. Dominance of AI Inference Workloads ~AI inference constitutes 80% of AI workloads in cloud environments, with latency-sensitive applications like chatbots, recommendation engines, and fraud detection requiring sub-second response times. ~"Venice's" 35x inference performance uplift directly addresses these requirements, outperforming Intel's offerings and custom Arm solutions in multi-threaded scenarios. B. Cost Efficiency and Operational Savings ~Azure's 2025 capex of $118B is under pressure to deliver returns. "Venice" can reduce operational expenses by $20-30B annually due to its power efficiency and performance gains, improving Azure's margins to 35-40%. ~The cost per inference operation is significantly lower with "Venice," estimated at 24-31% less than Intel-based alternatives, enhancing Azure's competitiveness against AWS and GCP. C. Scalability for Enterprise AI: ~"Venice" supports rack-scale AI deployments, enabling Azure to scale AI services for enterprise customers. For example, a 1,000-node cluster can process 700,000+ tokens per second, crucial for large-scale AI applications like personalized marketing and predictive analytics. ~This scalability is particularly important as Azure aims to capture the $100B+ AI opportunity by 2026, as stated by Microsoft CEO Satya Nadella. D. Reduction of Nvidia Dependency ~While Nvidia ( $NVDA) dominates AI accelerators, AMD's integrated EPYC-GPU solutions (MI450 with "Venice") offer a balanced approach, reducing Azure's reliance on Nvidia's high-cost GPUs. ~"Venice" enables hybrid inference models, where CPU-based inference handles 80% of workloads, and GPU acceleration is reserved for training and complex tasks, optimizing resource allocation. 3. Financial Implication: ~Revenue from Azure could reach $15-18B annually by 2026, part of a total revenue projection of $70-100B ~Profit margins could improve to 55-60%, boosting net income to $20-25B, supported by scale economies and reduced production costs. Intel could respond by giving more aggressive discounts, but this breakthrough has been a decade long of $AMD R&D, or rethinking chiplet design, a complete new approach. "Venice's" lead in AI inference and efficiency is challenging to match. Broader Industry: Other hyperscalers ( Amazon Web Services , GCP) and enterprises will follow Azure's lead, standardizing EPYC technology and pressuring Intel further. This could lead to a broader industry shift towards AMD, enhancing its ecosystem and bargaining power. Conclusion: The strategic adoption of AMD's 6th Generation EPYC "Venice" processors by Microsoft Azure in 2026 marks a pivotal moment in the evolution of cloud computing, particularly for AI inference capabilities. "Venice's" groundbreaking chiplet design, offering a 35x performance uplift for AI inference tasks, a 50% reduction in power consumption, and unparalleled scalability, positions Azure to leapfrog its competitors in the race for AI dominance. This technical superiority, combined with significant cost savings potentially $20-30B annually in operational expenses; aligns perfectly with Microsoft's ambitions to capture the $100B+ Revenue AI opportunity by 2026. The shift to 50% x86 market share for AMD within Azure is not merely a technical transition but a strategic realignment that redefines the competitive landscape. Historically, Microsoft's partnership with AMD has evolved from niche deployments to a core component of Azure's infrastructure, and "Venice" accelerates this trend. The 30-35% AMD EPYC share in 2025 is expected to double, driven by new VM families like C4D and H4D, which will dominate AI-intensive and HPC workloads. This migration is incentivized by "Venice's" efficiency gains, reducing dependency on Intel and Nvidia, and enhancing Azure's sustainability profile. Not Financial Advice!

Mike

141,018 просмотров • 9 месяцев назад

Journey with us toward the #Valhalla Mainnet launch in Q1 2025, as we continue enhancing the Valhalla game experience. Our latest release, Patch 0.29.0, introduced an array of exciting features and optimizations. Check out the video below to see this new patch in action! 👇 --- Some of the most important updates: Floki Tiers Introducing a groundbreaking system that boosts rewards for battles, shopping, and more. Currently, all players are set to the same tier in Testnet. Loot Chests Earn rewards in battles! Collect chests of varying rarities and open them at The Blacksmith. For now, chests grant Runix, but bigger prizes are coming soon. B’s Shop Returns The Shopkeeper is back with new items and a Shop Reputation System. Trade regularly to earn better discounts and sell values, but don’t let your streak decay! Pause Menu & Account Page: A sleek new pause menu connects to an account page for managing your BNB balance—perfect for preparing for the Mainnet launch. AI Improvements in Vera Encounters Veras now hit back with their abilities, adding an extra layer of challenge to battles. Full patch notes can be found here: Jump in and play now at:
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Journey with us toward the #Valhalla Mainnet launch in Q1 2025, as we continue enhancing the Valhalla game experience. Our latest release, Patch 0.29.0, introduced an array of exciting features and optimizations. Check out the video below to see this new patch in action! 👇 --- Some of the most important updates: Floki Tiers Introducing a groundbreaking system that boosts rewards for battles, shopping, and more. Currently, all players are set to the same tier in Testnet. Loot Chests Earn rewards in battles! Collect chests of varying rarities and open them at The Blacksmith. For now, chests grant Runix, but bigger prizes are coming soon. B’s Shop Returns The Shopkeeper is back with new items and a Shop Reputation System. Trade regularly to earn better discounts and sell values, but don’t let your streak decay! Pause Menu & Account Page: A sleek new pause menu connects to an account page for managing your BNB balance—perfect for preparing for the Mainnet launch. AI Improvements in Vera Encounters Veras now hit back with their abilities, adding an extra layer of challenge to battles. Full patch notes can be found here: Jump in and play now at:

FLOKI

40,259 просмотров • 1 год назад

🌟 Another Epic Sneak Peek: OptimAI Core Node’s Edge AI Computing Power! We’re excited to continue unveiling the upcoming OptimAI Core Node features! This time, we’re introducing Edge AI Computing, a game-changing feature that will make your devices even more powerful in contributing to the OptimAI Network. 🔥 What is Edge AI Computing? With the OptimAI Core Node, your device’s idle computing resources (CPU/GPU) and storage will be put to work, powering critical AI computing tasks, including: 🔸Edge Inference: Running real-time AI predictions directly on your device without sending data to centralized servers. 🔸Hot LLM Models: Accelerating large language models for high-speed NLP tasks. 🔸Generative AI Models and More: Contributing to the training and inference of cutting-edge generative AI, like text-to-image models, deep learning, and more. More Contributions, Bigger Rewards Await! This Edge AI Computing feature allows us to leverage the full potential of decentralized computing power, reducing latency, increasing efficiency, and making AI more accessible. The more you contribute, the bigger the rewards!🔥 Stay in the Loop—Start Today with OptimAI Lite Node! While the OptimAI Core Node is still in the works, you don’t have to wait to join the action. The more you contribute, the greater the rewards! Let’s build a stronger, smarter, and more decentralized AI network together. Stay tuned for more exciting updates! 🔸Extension Node: 🔸Telegram Node: 🔸Register at: #DePIN
0:30

🌟 Another Epic Sneak Peek: OptimAI Core Node’s Edge AI Computing Power! We’re excited to continue unveiling the upcoming OptimAI Core Node features! This time, we’re introducing Edge AI Computing, a game-changing feature that will make your devices even more powerful in contributing to the OptimAI Network. 🔥 What is Edge AI Computing? With the OptimAI Core Node, your device’s idle computing resources (CPU/GPU) and storage will be put to work, powering critical AI computing tasks, including: 🔸Edge Inference: Running real-time AI predictions directly on your device without sending data to centralized servers. 🔸Hot LLM Models: Accelerating large language models for high-speed NLP tasks. 🔸Generative AI Models and More: Contributing to the training and inference of cutting-edge generative AI, like text-to-image models, deep learning, and more. More Contributions, Bigger Rewards Await! This Edge AI Computing feature allows us to leverage the full potential of decentralized computing power, reducing latency, increasing efficiency, and making AI more accessible. The more you contribute, the bigger the rewards!🔥 Stay in the Loop—Start Today with OptimAI Lite Node! While the OptimAI Core Node is still in the works, you don’t have to wait to join the action. The more you contribute, the greater the rewards! Let’s build a stronger, smarter, and more decentralized AI network together. Stay tuned for more exciting updates! 🔸Extension Node: 🔸Telegram Node: 🔸Register at: #DePIN

OptimAI Network

44,302 просмотров • 1 год назад

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026
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$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

Mike

44,460 просмотров • 5 месяцев назад

XDC Pulse | September 2025 Edition A landmark month for the XDC Network, where interoperability, liquidity, and global adoption came together to define the next phase of blockchain evolution. From Circle 's USDC and CCTP V2 going live on XDC, to Chainlink CCIP Tracker integration, and major exchange listings on Kraken and etoro , September was a month that set new benchmarks for trust, accessibility, and institutional growth. Highlights include: - Native USDC integration enabling seamless, low-cost digital dollar settlements - Chainlink CCIP connecting XDC to 15+ blockchain networks - Major exchange listings on Kraken and eToro - Orochi Network’s zkDA partnership is reducing data costs for tokenization - XVC's $1M investment in Kasu - Global presence across Boston, Seoul, Zurich, Riyadh, Australia and beyond As we head into October, the momentum continues — with TOKEN2049 Singapore, the Euronext Bell Ceremony, GITEX GLOBAL in Dubai, and more global milestones ahead.
8:56

XDC Pulse | September 2025 Edition A landmark month for the XDC Network, where interoperability, liquidity, and global adoption came together to define the next phase of blockchain evolution. From Circle 's USDC and CCTP V2 going live on XDC, to Chainlink CCIP Tracker integration, and major exchange listings on Kraken and etoro , September was a month that set new benchmarks for trust, accessibility, and institutional growth. Highlights include: - Native USDC integration enabling seamless, low-cost digital dollar settlements - Chainlink CCIP connecting XDC to 15+ blockchain networks - Major exchange listings on Kraken and eToro - Orochi Network’s zkDA partnership is reducing data costs for tokenization - XVC's $1M investment in Kasu - Global presence across Boston, Seoul, Zurich, Riyadh, Australia and beyond As we head into October, the momentum continues — with TOKEN2049 Singapore, the Euronext Bell Ceremony, GITEX GLOBAL in Dubai, and more global milestones ahead.

XDC Network

162,870 просмотров • 9 месяцев назад

I really don't post wins much on this app 🤣🤣 it feels good being low-key. Although, let's doxx this one!. Most of the time, I win big, I reinvest, win again, reinvest. I barely spend on myself much, mostly spend my money helping people, or supporting the needy. It's time for me 😌. Hey NVIDIA GeForce Hey ROG Global I Just slapped a Brand new heavy machine in my bag! One of the most powerful laptops of 2025! Emphasis on BRAND NEW! Introducing, the new owner of the ASUS ROG STRIX G16 2025. SPECS: - Intel Core Ultra 9 275HX Processor - 32GB DDR5 RAM - Nvidia GeForce RTX™ 5080 Dedicated GPU with a massive 16GB DDR7 VRAM - 3TB PCIe 4.0 SSD NVME storage - 24 Cores CPU (16 E-cores + 8 P-cores) - Base Frequency 2.70GHz - Max Turbo Frequency 5.40GHz 6 Million Naira well spent on this bad boy. Now, let's lock-in again and run it up back turbo! I need my $U bag soon enough 🤭. Pitch me on to node running, and prover projects, I'm fully locked in.
0:21

I really don't post wins much on this app 🤣🤣 it feels good being low-key. Although, let's doxx this one!. Most of the time, I win big, I reinvest, win again, reinvest. I barely spend on myself much, mostly spend my money helping people, or supporting the needy. It's time for me 😌. Hey NVIDIA GeForce Hey ROG Global I Just slapped a Brand new heavy machine in my bag! One of the most powerful laptops of 2025! Emphasis on BRAND NEW! Introducing, the new owner of the ASUS ROG STRIX G16 2025. SPECS: - Intel Core Ultra 9 275HX Processor - 32GB DDR5 RAM - Nvidia GeForce RTX™ 5080 Dedicated GPU with a massive 16GB DDR7 VRAM - 3TB PCIe 4.0 SSD NVME storage - 24 Cores CPU (16 E-cores + 8 P-cores) - Base Frequency 2.70GHz - Max Turbo Frequency 5.40GHz 6 Million Naira well spent on this bad boy. Now, let's lock-in again and run it up back turbo! I need my $U bag soon enough 🤭. Pitch me on to node running, and prover projects, I'm fully locked in.

🐐𝙲𝚛𝚢𝚙𝚝𝚘𝙳𝚛𝚊𝚐𝚘𝚗「🦑」

18,821 просмотров • 11 месяцев назад

At its core, iAgent utilizes Protocol Nodes to support and maintain the ecosystem's functionality. This collection of publicly maintained nodes help: ❒ secure the network ❒ determine rewards for GPU workers ❒ earn rewards for their contributions to the network iAgent has 2 types of protocol nodes: 🔘Genesis Nodes 🔘Public Nodes With a 5000-supply, Genesis Nodes are engineered to yield x3 $AGNT tokens generation compared to Public Nodes (which will come at a later date). As a token of gratitude, Genesis Nodes also receive TGE: 2% AGNT tokens from reward pool. Other Genesis Node Utilities & USPs include: Participation and Governance: Genesis node operators have the chance to engage more deeply in decision-making processes through iAgent's governance model. Exclusive Access: Operators of Genesis nodes may receive early access to test new features and products within the iAgent ecosystem. Reward Structure: Nodes earn $AGNT tokens, and Genesis nodes do so at an accelerated rate due to their higher token generation multiplier. Rewards are calculated dynamically, with consideration given to node performance and network contribution. Technical and Community Support: Genesis node operators are expected to receive dedicated support from the iAgent team, ensuring optimal performance and uptime. Requirements to run an iAgent Node: Dual Core, and 8GB RAM, are all that is needed for lightweight processing. Anyone with Genesis/Public Node NFTs can run it. But how do you get yourself one? Hop in the discord to know more:
2:15

At its core, iAgent utilizes Protocol Nodes to support and maintain the ecosystem's functionality. This collection of publicly maintained nodes help: ❒ secure the network ❒ determine rewards for GPU workers ❒ earn rewards for their contributions to the network iAgent has 2 types of protocol nodes: 🔘Genesis Nodes 🔘Public Nodes With a 5000-supply, Genesis Nodes are engineered to yield x3 $AGNT tokens generation compared to Public Nodes (which will come at a later date). As a token of gratitude, Genesis Nodes also receive TGE: 2% AGNT tokens from reward pool. Other Genesis Node Utilities & USPs include: Participation and Governance: Genesis node operators have the chance to engage more deeply in decision-making processes through iAgent's governance model. Exclusive Access: Operators of Genesis nodes may receive early access to test new features and products within the iAgent ecosystem. Reward Structure: Nodes earn $AGNT tokens, and Genesis nodes do so at an accelerated rate due to their higher token generation multiplier. Rewards are calculated dynamically, with consideration given to node performance and network contribution. Technical and Community Support: Genesis node operators are expected to receive dedicated support from the iAgent team, ensuring optimal performance and uptime. Requirements to run an iAgent Node: Dual Core, and 8GB RAM, are all that is needed for lightweight processing. Anyone with Genesis/Public Node NFTs can run it. But how do you get yourself one? Hop in the discord to know more:

iAgent

21,278 просмотров • 1 год назад

$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.
0:49

$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.

Mike

511,082 просмотров • 9 месяцев назад

A new roadmap. A New Era of The Graph 🗺️ The Graph’s new roadmap introduces a bold and transformative vision for the future of The Graph! The new R&D roadmap details an expansion of The Graph’s ability to serve web3’s growing demands for data access, while better serving builders and protocol contributors, and improving the overall simplicity and efficiency of the network. After three years of serving builders, The Graph Network is mature, reliable, and performant. The Graph ecosystem has followed through on its commitment to democratize access to blockchain data while also establishing subgraphs as a web3 standard. But The Graph’s innovation journey doesn’t end there. The New Era of The Graph is organized into five core objectives: 1️⃣ World of Data Services: Expanding to provide new data services beyond subgraphs to deliver a rich market of data on the network, serving novel use cases for data scientists and more. This will include more data sources, new query languages, and support for LLMs. 2️⃣ Developer Empowerment: Supporting developers through enhanced DevEx and tooling by introducing streamlined billing, clear pricing models, a new free query plan, and reduced gas fees. A more SaaS-like experience for devs, without compromising on decentralization! 3️⃣ Protocol Evolution & Resiliency: Delivering improvements resulting in a more resilient, flexible, and simple protocol, including updates to delegation. 4️⃣ Optimized Indexer Performance: Boosting network performance with improved Indexer tooling and operational capabilities to deliver increased scalability, reduce costs, and enhanced network reliability. 5️⃣ Interconnected Graph of Data: Creating tools for composable data and a global, organized knowledge graph – interlinking open data and making it easier to build upon. The new roadmap sets in motion an exciting evolution in web3 data infrastructure. In a phased rollout, The Graph will introduce many new features and benefits, including the integration of new data services, new query languages, enhanced developer tooling, improved UX + UI, alongside greater protocol efficiency and resilience. As this new era unfolds, The Graph crystallizes as the connective tissue across the many layers of the web3 stack, evolving into a comprehensive, interwoven graph of data equipped to serve every project dreamt up by web3’s innovators. Read the full announcement linked in the comment below!
0:47

A new roadmap. A New Era of The Graph 🗺️ The Graph’s new roadmap introduces a bold and transformative vision for the future of The Graph! The new R&D roadmap details an expansion of The Graph’s ability to serve web3’s growing demands for data access, while better serving builders and protocol contributors, and improving the overall simplicity and efficiency of the network. After three years of serving builders, The Graph Network is mature, reliable, and performant. The Graph ecosystem has followed through on its commitment to democratize access to blockchain data while also establishing subgraphs as a web3 standard. But The Graph’s innovation journey doesn’t end there. The New Era of The Graph is organized into five core objectives: 1️⃣ World of Data Services: Expanding to provide new data services beyond subgraphs to deliver a rich market of data on the network, serving novel use cases for data scientists and more. This will include more data sources, new query languages, and support for LLMs. 2️⃣ Developer Empowerment: Supporting developers through enhanced DevEx and tooling by introducing streamlined billing, clear pricing models, a new free query plan, and reduced gas fees. A more SaaS-like experience for devs, without compromising on decentralization! 3️⃣ Protocol Evolution & Resiliency: Delivering improvements resulting in a more resilient, flexible, and simple protocol, including updates to delegation. 4️⃣ Optimized Indexer Performance: Boosting network performance with improved Indexer tooling and operational capabilities to deliver increased scalability, reduce costs, and enhanced network reliability. 5️⃣ Interconnected Graph of Data: Creating tools for composable data and a global, organized knowledge graph – interlinking open data and making it easier to build upon. The new roadmap sets in motion an exciting evolution in web3 data infrastructure. In a phased rollout, The Graph will introduce many new features and benefits, including the integration of new data services, new query languages, enhanced developer tooling, improved UX + UI, alongside greater protocol efficiency and resilience. As this new era unfolds, The Graph crystallizes as the connective tissue across the many layers of the web3 stack, evolving into a comprehensive, interwoven graph of data equipped to serve every project dreamt up by web3’s innovators. Read the full announcement linked in the comment below!

The Graph

425,373 просмотров • 2 лет назад

New Mark Cerny PS5 Pro Technical Seminar. Some interesting info. GPU is a hybrid of RDNA tech Base tech - RDNA2.X (RDNA2 + 3) Ray Tracing - RDNA4 Machine Learning - Custom Not fully RDNA3 to reduce workload for ports/code. Doesn't have RDNA3 shader executables and double floating point calculations, though it does have other RDNA3 and custom features such as extra vertex and primitive processing, more shader instructions for ML etc. Not having double FP is why Tflop count is 16.7 Tflop and not 33.5 Tflop, but Tflop count difference is "meaningless", esp to real world performance, and they didn't need 32bit calculation bump. Though interestingly PS5 Pro still retains 8 operations per lane for 67 Tflops of 16bit performance. Custom ML hardware has 300 TOPS of performance. PS5 Pro also has over 1GB of extra GDDR6 ram for games vs PS5, thanks to extra DDR5 system ram that many OS operations have been moved on to. Ray Tracing is roughly 2-3x faster than equivalent PS5 performance. ML hardware are PlayStation custom additions to RDNA that they themselves designed in collaboration with AMD. #PS5Pro #PS5 Full video below
6:07

New Mark Cerny PS5 Pro Technical Seminar. Some interesting info. GPU is a hybrid of RDNA tech Base tech - RDNA2.X (RDNA2 + 3) Ray Tracing - RDNA4 Machine Learning - Custom Not fully RDNA3 to reduce workload for ports/code. Doesn't have RDNA3 shader executables and double floating point calculations, though it does have other RDNA3 and custom features such as extra vertex and primitive processing, more shader instructions for ML etc. Not having double FP is why Tflop count is 16.7 Tflop and not 33.5 Tflop, but Tflop count difference is "meaningless", esp to real world performance, and they didn't need 32bit calculation bump. Though interestingly PS5 Pro still retains 8 operations per lane for 67 Tflops of 16bit performance. Custom ML hardware has 300 TOPS of performance. PS5 Pro also has over 1GB of extra GDDR6 ram for games vs PS5, thanks to extra DDR5 system ram that many OS operations have been moved on to. Ray Tracing is roughly 2-3x faster than equivalent PS5 performance. ML hardware are PlayStation custom additions to RDNA that they themselves designed in collaboration with AMD. #PS5Pro #PS5 Full video below

NIB

52,971 просмотров • 1 год назад

If you care about climate action, this one's for you. Since we started Octant we've been funding public goods and open-source projects, and we've been working on bigger plans to be able to bring positive and lasting change. 6 epochs later, 65+ projects funded, and $6M distributed, we're excited to announce a major milestone for Epoch 7... 🥁🥁🥁 Introducing the Octant Climate Epoch, in partnership with Climate Coordination Network (CCN) Climate Coordination Network 🌍🌡️This is our first themed round, and it’s all about tackling the climate crisis. 🌡️Why climate and why now? Because if not now, when? The climate crisis is urgent, and action is a collective responsibility. This epoch is our way of empowering builders solving one of humanity’s biggest challenges. 💡Are we climate experts? No. But the folks at Climate Coordination Network are. They’ve been leading climate funding rounds since 2021, crowdfunded $1M+, and distributed $4.5M to web3 projects tackling climate change. Together, we’re here to support the ultimate public good. 💚Why this epoch matters: this is more than funding—it’s a step towards Octant v2, a protocol that helps ecosystems sustainably fund their communities while supporting essential public goods. This epoch will test new mechanisms for v2, designed to better serve high-quality donors and projects. ⭐️So what’s new? — No snapshot for this epoch. Projects will be handpicked with CCN. — The allocation process remains the same for donors, but with curated projects focused on climate. — Testing new tools + processes to improve outcomes for projects and donors. We'll share more on project eligibility and criteria over the next few weeks. What's not changing? ✅ The user rewards in this epoch stay the same. ✅ We'll be back to our regular programming with Epoch 8 being a regular epoch. 📈Funding is just the start. Over the next year, we’re providing mentorship, resources, and long-term support for projects in this epoch. We'll also work with independent experts who will evaluate the impact of these efforts.
0:51

If you care about climate action, this one's for you. Since we started Octant we've been funding public goods and open-source projects, and we've been working on bigger plans to be able to bring positive and lasting change. 6 epochs later, 65+ projects funded, and $6M distributed, we're excited to announce a major milestone for Epoch 7... 🥁🥁🥁 Introducing the Octant Climate Epoch, in partnership with Climate Coordination Network (CCN) Climate Coordination Network 🌍🌡️This is our first themed round, and it’s all about tackling the climate crisis. 🌡️Why climate and why now? Because if not now, when? The climate crisis is urgent, and action is a collective responsibility. This epoch is our way of empowering builders solving one of humanity’s biggest challenges. 💡Are we climate experts? No. But the folks at Climate Coordination Network are. They’ve been leading climate funding rounds since 2021, crowdfunded $1M+, and distributed $4.5M to web3 projects tackling climate change. Together, we’re here to support the ultimate public good. 💚Why this epoch matters: this is more than funding—it’s a step towards Octant v2, a protocol that helps ecosystems sustainably fund their communities while supporting essential public goods. This epoch will test new mechanisms for v2, designed to better serve high-quality donors and projects. ⭐️So what’s new? — No snapshot for this epoch. Projects will be handpicked with CCN. — The allocation process remains the same for donors, but with curated projects focused on climate. — Testing new tools + processes to improve outcomes for projects and donors. We'll share more on project eligibility and criteria over the next few weeks. What's not changing? ✅ The user rewards in this epoch stay the same. ✅ We'll be back to our regular programming with Epoch 8 being a regular epoch. 📈Funding is just the start. Over the next year, we’re providing mentorship, resources, and long-term support for projects in this epoch. We'll also work with independent experts who will evaluate the impact of these efforts.

Octant

10,859 просмотров • 1 год назад

Introducing Neural Capture Version 2 on CorOS 3.3.0 and NanOS 2.2.0 - available now! Neural Capture Version 2 is a new cloud-trained version of Neural Capture that delivers higher resolution, greater realism, and improved dynamic response. By shifting the training process to Cortex Cloud, Capture V2 uses a more advanced algorithm that can model complex analog behavior beyond what is possible with on-device processing. Capture V2 brings major improvements to devices that rely heavily on touch and dynamics. These devices are notoriously difficult to capture accurately, making V2 the most authentic solution on the market for reproducing the dynamic cleanup behavior of a vintage fuzz, the natural bloom of a sagging power amp, and the fast transient response of a studio compressor. V2 Capture creation is currently available only on Quad Cortex. NanOS 2.2.0 introduces the Capture 2 Player, which lets Nano Cortex load and play V2 Captures created on Quad Cortex. Support for creating V2 Captures directly on Nano Cortex is under development. CorOS 3.3.0 🔥 Neural Capture Version 2 🔥 669 V2 Captures across 41 devices 🔥 29 new virtual devices: ⚙️⚙️ Dumbbell ODS (Dumble® Overdrive Special®) ⚙️⚙️ 17 Cabs ⚙️⚙️ Mono Synth ⚙️⚙️ Micro Processor (ST) (Eventide® Micropitch Delay®) ⚙️⚙️ Pattern Tremolo ⚙️⚙️ Bit-Crusher Engine (M) ⚙️⚙️ Bit-Crusher (ST) ⚙️⚙️ Phase-Locked Loop (EarthQuaker Devices® Data Corrupter®) ⚙️⚙️ 81 Creations Drive (1981 Inventions® DRV®) ⚙️⚙️ Aggi Sub Octaver (Aguilar® Octamizer®) ⚙️⚙️ Spring Reverb Engine (M) ⚙️⚙️ Spring Reverb Engine (ST) ⚙️⚙️ Auto Wah 🔥 Several quality of life improvements Cortex Control 1.4.0 Cortex Control has been updated to support CorOS 3.3.0. To create a Neural Capture V2, you need the latest versions of CorOS and Cortex Control. NanOS 2.2.0 🔥 Neural Capture Version 2 🔥 669 V2 Captures across 41 devices (downloadable from the official Neural DSP Cortex Cloud profile) 🔥 Cortex Cloud offline mode 🔥 Automatic Sum to Mono 🔥 Tremolo 🔥 Capture auditioning 🔥 Cloud backups Read everything about this huge update here: 🗞️
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Introducing Neural Capture Version 2 on CorOS 3.3.0 and NanOS 2.2.0 - available now! Neural Capture Version 2 is a new cloud-trained version of Neural Capture that delivers higher resolution, greater realism, and improved dynamic response. By shifting the training process to Cortex Cloud, Capture V2 uses a more advanced algorithm that can model complex analog behavior beyond what is possible with on-device processing. Capture V2 brings major improvements to devices that rely heavily on touch and dynamics. These devices are notoriously difficult to capture accurately, making V2 the most authentic solution on the market for reproducing the dynamic cleanup behavior of a vintage fuzz, the natural bloom of a sagging power amp, and the fast transient response of a studio compressor. V2 Capture creation is currently available only on Quad Cortex. NanOS 2.2.0 introduces the Capture 2 Player, which lets Nano Cortex load and play V2 Captures created on Quad Cortex. Support for creating V2 Captures directly on Nano Cortex is under development. CorOS 3.3.0 🔥 Neural Capture Version 2 🔥 669 V2 Captures across 41 devices 🔥 29 new virtual devices: ⚙️⚙️ Dumbbell ODS (Dumble® Overdrive Special®) ⚙️⚙️ 17 Cabs ⚙️⚙️ Mono Synth ⚙️⚙️ Micro Processor (ST) (Eventide® Micropitch Delay®) ⚙️⚙️ Pattern Tremolo ⚙️⚙️ Bit-Crusher Engine (M) ⚙️⚙️ Bit-Crusher (ST) ⚙️⚙️ Phase-Locked Loop (EarthQuaker Devices® Data Corrupter®) ⚙️⚙️ 81 Creations Drive (1981 Inventions® DRV®) ⚙️⚙️ Aggi Sub Octaver (Aguilar® Octamizer®) ⚙️⚙️ Spring Reverb Engine (M) ⚙️⚙️ Spring Reverb Engine (ST) ⚙️⚙️ Auto Wah 🔥 Several quality of life improvements Cortex Control 1.4.0 Cortex Control has been updated to support CorOS 3.3.0. To create a Neural Capture V2, you need the latest versions of CorOS and Cortex Control. NanOS 2.2.0 🔥 Neural Capture Version 2 🔥 669 V2 Captures across 41 devices (downloadable from the official Neural DSP Cortex Cloud profile) 🔥 Cortex Cloud offline mode 🔥 Automatic Sum to Mono 🔥 Tremolo 🔥 Capture auditioning 🔥 Cloud backups Read everything about this huge update here: 🗞️

Neural DSP

36,094 просмотров • 8 месяцев назад