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Thursday Note to Subscribers: $AMD “Our Top Idea into Friday, Pure High Confluence look for move to $250 -> $253” AMD 4-10 240C: +900% ($9 ITM) AMD 4-17 270C: +355% 🦠

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30,386 Aufrufe • vor 3 Monaten •via X (Twitter)

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Billionaire Bill Ackman’s best advice to investors in 2026… Sounds like we should just buy: 1) $VOO 2) $QQQM 3) $NVDA 4) $AMD 5) $MSFT 6) $SCHG 7) $MAGS 8) $SMH 9) $DRAM 10) $GOOG

Marcos Milla

97,884 Aufrufe • vor 2 Monaten

Some time ago, I had the idea to port NVIDIA Physical AI stack to AMD. The motivation was to improve hardware diversity and enable world models and VLAs to run beyond a single ecosystem. We started with NVIDIA Cosmos Predict 2.5-2B. Porting wasn’t trivial: these models are deeply optimized for NVIDIA’s stack. We used this as an opportunity to apply our ROCm kernels. The results were surprising: Both encode and diffusion run faster on AMD Instinct MI300X vs. NVIDIA H200 (FA3) and we still saw significant headroom for further optimization. Quality is unchanged across modalities (validated with WorldJen) To be clear, this is no luck. We have deep experience with diffusion models and AMD GPUs. But this just gives us a good opportunity to get closer to a true hardware-to-hardware comparison, as we work with less software abstractions than usual. Just to give an example, on AMD, memory instructions are async with a hardware queue of ordered pending instructions, enabling concurrent load/store with compute without warp specialization. Bottom line: there are real architectural advantages on AMD, if you take the time to work with the hardware. Note, we did tradeoff ~20% higher memory usage, That being said, AMD has more to give to begin with :) in the coming weeks: AMD versions of Cosmos Transfer and GR00T, an even faster version of Cosmos Predict, and open-sourcing an attention kernel faster than AITER v3 (which is closed-source for some reason? cc: Anush Elangovan )

Some time ago, I had the idea to port NVIDIA Physical AI stack to AMD. The motivation was to improve hardware diversity and enable world models and VLAs to run beyond a single ecosystem. We started with NVIDIA Cosmos Predict 2.5-2B. Porting wasn’t trivial: these models are deeply optimized for NVIDIA’s stack. We used this as an opportunity to apply our ROCm kernels. The results were surprising: Both encode and diffusion run faster on AMD Instinct MI300X vs. NVIDIA H200 (FA3) and we still saw significant headroom for further optimization. Quality is unchanged across modalities (validated with WorldJen) To be clear, this is no luck. We have deep experience with diffusion models and AMD GPUs. But this just gives us a good opportunity to get closer to a true hardware-to-hardware comparison, as we work with less software abstractions than usual. Just to give an example, on AMD, memory instructions are async with a hardware queue of ordered pending instructions, enabling concurrent load/store with compute without warp specialization. Bottom line: there are real architectural advantages on AMD, if you take the time to work with the hardware. Note, we did tradeoff ~20% higher memory usage, That being said, AMD has more to give to begin with :) in the coming weeks: AMD versions of Cosmos Transfer and GR00T, an even faster version of Cosmos Predict, and open-sourcing an attention kernel faster than AITER v3 (which is closed-source for some reason? cc: Anush Elangovan )

Omer Shlomovits

36,593 Aufrufe • vor 3 Monaten

$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. 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. Challenges and Risks While effective, AMD’s lower price strategy carries risks: ~Profitability Concerns: Lower prices can compress profit margins, and some analysts note that AMD’s high stock valuation expects future profitability that may be delayed if pricing remains aggressive. ~Perception of Quality: Persistently low prices risk positioning AMD as a “budget” brand, potentially undermining its ability to compete in premium segments. ~Competitor Response: Intel and Nvidia can counter with price cuts or superior features, as seen with Nvidia’s feature-rich GPUs. AMD must balance price with innovation to avoid being outmaneuvered. 8. Strategic Shift in GPUs Recent reports indicate AMD is adjusting its GPU strategy to prioritize market share over competing in the high-end enthusiast segment. For the upcoming Radeon RX 8000 series (RDNA 4), AMD is focusing on mainstream GPUs priced competitively to appeal to a broader audience, rather than chasing Nvidia’s high-end dominance. 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.

Mike

38,006 Aufrufe • vor 10 Monaten

The official release of Once Human is set for July 9th, 2024 (Pacific Time)! To celebrate, we're hosting a giveaway! ┉┉▾Giveaway▾┉┉ Follow Once Human and repost this post to join. • Event period: Before 23:59 June 17th PT. • Basic Rewards: - Steam Deck 1 TB OLED×1 - Steam Gift Card: $100×5 - Steam Gift Card: $50×10 And that's not all! If any of the following milestones are hit, the prizes get even better! • Giveaway Upgrades: - Reach 3,000 engagements (likes, comments, reposts): + Steam Deck×1 - Reach 50,000 followers: + Steam Decks×1 - During Steam Next Fest, if Once Human reaches Steam's "Most Played" Top 10: + AMD×Once Human limited co-branded host [AMD Radeon™ RX 7900 Series GPU, AMD Ryzen™ 9 7800X3D CPU] ×1 Don't forget to wishlist now and pre-download our Steam Next Fest demo starting June 7th. Get ready to start playing at 10:00 AM on June 10th PT! Wishlist on Steam: #OnceHuman

The official release of Once Human is set for July 9th, 2024 (Pacific Time)! To celebrate, we're hosting a giveaway! ┉┉▾Giveaway▾┉┉ Follow Once Human and repost this post to join. • Event period: Before 23:59 June 17th PT. • Basic Rewards: - Steam Deck 1 TB OLED×1 - Steam Gift Card: $100×5 - Steam Gift Card: $50×10 And that's not all! If any of the following milestones are hit, the prizes get even better! • Giveaway Upgrades: - Reach 3,000 engagements (likes, comments, reposts): + Steam Deck×1 - Reach 50,000 followers: + Steam Decks×1 - During Steam Next Fest, if Once Human reaches Steam's "Most Played" Top 10: + AMD×Once Human limited co-branded host [AMD Radeon™ RX 7900 Series GPU, AMD Ryzen™ 9 7800X3D CPU] ×1 Don't forget to wishlist now and pre-download our Steam Next Fest demo starting June 7th. Get ready to start playing at 10:00 AM on June 10th PT! Wishlist on Steam: #OnceHuman

Once Human

1,832,672 Aufrufe • vor 2 Jahren

$AMD $NVDA & the AMD Bear SemiAnalysis 🧵 Here are some facts: $META allocated 42% AI GPUs to $AMD OpenAI allocated 6GW(38%) to $AMD 1. Model-Specific Bias: Llama 3.3 70B graph favored NVIDIA due to TRT-LLM optimizations, highlighting throughput and latency where Blackwell excels. In contrast, the GPT-OSS 120B chart shifts focus to cost and interactivity, where MI355X shines. This selective model choice clearly suggests SemiAnalysis tailors benchmarks to reinforce narratives—NVIDIA’s dominance in speed (Llama 3.3) and AMD’s niche in cost (GPT-OSS). GPT-OSS 120B, with its sparse attention mechanisms (similar to DeepSeek-V3.2-Exp), shows AMD’s CDNA 4 architecture, while Llama 3.3’s dense attention favors NVIDIA’s Tensor Cores. SemiAnalysis’ decision to emphasize Llama 3.3 initially could reflect its AMD bear stance. 2. The way Data is presented The Llama 3.3 graph focused on raw performance metrics (throughput vs. latency), downplaying cost, where AMD holds an edge. This new chart, buried in follow-up posts, reveals AMD’s strength but receives less prominence, suggesting a curated narrative. Labeling variability (e.g., B200 with/without TRT) and the lack of uniform scaling across graphs indicate potential cherry-picking of configurations to favor NVIDIA’s optimized setups. 3. Historical Context: SemiAnalysis’ past critiques of AMD’s R&D and ROCm (web results from May 2025) align with a bearish outlook. Their own hype/brand around NVIDIA’s 15x ROI contrasts with muted coverage of AMD’s cost advantages, reinforcing bias. Despite AMD’s participation in InferenceMAX, the benchmark’s framing (e.g., prioritizing Blackwell’s ROI) reflect SemiAnalysis’ market predictions rather than balanced analysis. Lastly, AMD’s Instinct MI355X proves superior in inference and cost per million tokens for the GPT-OSS 120B model, offering a 25% cost advantage over NVIDIA’s H200 at moderate-to-high interactivity levels. This efficiency, driven by AMD’s memory bandwidth and FP4 support, makes it a better choice for cost-sensitive, multi-user deployments over a three-year horizon. However, SemiAnalysis’ sole focus(presentation graph) on Llama 3.3—where NVIDIA excels demonstrates a pattern of cherry-picking models and data to favor NVIDIA , consistent with its historical AMD bearish stance. This selective presentation risks misleading stakeholders by overshadowing AMD economic strengths. My personal take: I would trust Dr. Lisa Su, and Greg Brockman Sam Altman take on AMD and how they viewed and allocated 6GW for AMD over SemiAnalysis . At the end of the day, Large customers pay when it works. $Meta allocated 42% AI GPUs to $AMD for a reason. And the "secret weapon" will improve energy consumption by 20-50%, meaning at 6GW, OpenAI would be able to deploy 25-50% more MI450 at a much better cost advantage, higher memory bandwidth, and the queen of Inference! Oh and ROCm 8 is expected to be on par with CUDA in 2026.

Mike

104,194 Aufrufe • vor 9 Monaten

No single vendor will win the AI race, but open ecosystems might. Real velocity in AI comes from interoperability, not lock-in. And AMD just made all of its software open source. At last week’s Advancing AI 2025, we sat down with AMD’s VP of AI Software Anush Elangovan and Sharon Zhou VP of AI at AMD, to discuss their case for why an open, multi-partner ecosystem will accelerate AI innovation faster than any proprietary alternative. AMD’s announcements last week double down on this OSS focus and their commitment to AI infrastructure, including: ✅ Open Source Ecosystem: ROCm 7, AMD’s latest open-source AI software stack, introduces kernel-level improvements for GEMM operations, optimized attention mechanisms, and expanded support for distributed inference. The update brings substantial speedups for inference workloads, with average performance increases of 3.2x to 3.8x ✅ Hardware: New MI355X GPU delivers up to 40% more tokens per dollar vs competition & the MI350 Series has seen a 35x generational leap in AI inference performance ✅ Infrastructure Investments: Oracle just committed to zettascale (‼️) clusters with up to 131,072 MI355X GPUs and AMD showcased their new $10 billion partnership with Saudi Arabian AI firm HUMAIN to build AI infrastructure, including data centers, powered by AMD chips. ✅ Partnership Momentum: 7 out of 10 top AI companies now run production workloads on AMD Instinct accelerators (including Meta, OpenAI, Microsoft & xAI) By inviting interoperability and contribution at every layer, AMD is enabling developers to build faster, optimize deeper, and deploy with flexibility. Listen to Anush and Sharon’s Chain of Thought Podcast episode with host Conor Bronsdon in the next tweet to get all the details and a deep dive into AMD’s strategy 👇

No single vendor will win the AI race, but open ecosystems might. Real velocity in AI comes from interoperability, not lock-in. And AMD just made all of its software open source. At last week’s Advancing AI 2025, we sat down with AMD’s VP of AI Software Anush Elangovan and Sharon Zhou VP of AI at AMD, to discuss their case for why an open, multi-partner ecosystem will accelerate AI innovation faster than any proprietary alternative. AMD’s announcements last week double down on this OSS focus and their commitment to AI infrastructure, including: ✅ Open Source Ecosystem: ROCm 7, AMD’s latest open-source AI software stack, introduces kernel-level improvements for GEMM operations, optimized attention mechanisms, and expanded support for distributed inference. The update brings substantial speedups for inference workloads, with average performance increases of 3.2x to 3.8x ✅ Hardware: New MI355X GPU delivers up to 40% more tokens per dollar vs competition & the MI350 Series has seen a 35x generational leap in AI inference performance ✅ Infrastructure Investments: Oracle just committed to zettascale (‼️) clusters with up to 131,072 MI355X GPUs and AMD showcased their new $10 billion partnership with Saudi Arabian AI firm HUMAIN to build AI infrastructure, including data centers, powered by AMD chips. ✅ Partnership Momentum: 7 out of 10 top AI companies now run production workloads on AMD Instinct accelerators (including Meta, OpenAI, Microsoft & xAI) By inviting interoperability and contribution at every layer, AMD is enabling developers to build faster, optimize deeper, and deploy with flexibility. Listen to Anush and Sharon’s Chain of Thought Podcast episode with host Conor Bronsdon in the next tweet to get all the details and a deep dive into AMD’s strategy 👇

Galileo

78,922 Aufrufe • vor 1 Jahr

I told you to buy all Mag 7's in 2025. We're up over 72% since then. Now, I am telling you to stay patient for the red days which will start next week. Buy the dip when it happens. Here's exactly when and where to buy my top 10: 1. Amazon $AMZN 2. Apple $AAPL 3. Advanced Micro Devices $AMD 4. Nvidia $NVDA 5. Meta Platforms $META 6. Microsoft $MSFT 7. Google $GOOGL 8. Tesla $TSLA 9. Netflix $NFLX 10. ExxonMobil $XOM All my buy and sell signals in Discord @

I told you to buy all Mag 7's in 2025. We're up over 72% since then. Now, I am telling you to stay patient for the red days which will start next week. Buy the dip when it happens. Here's exactly when and where to buy my top 10: 1. Amazon $AMZN 2. Apple $AAPL 3. Advanced Micro Devices $AMD 4. Nvidia $NVDA 5. Meta Platforms $META 6. Microsoft $MSFT 7. Google $GOOGL 8. Tesla $TSLA 9. Netflix $NFLX 10. ExxonMobil $XOM All my buy and sell signals in Discord @

StockWhale

218,450 Aufrufe • vor 2 Monaten

Building AI infrastructure at yotta-scale requires more than raw performance; it demands an open, modular rack design that can evolve across product generations, combining leadership compute engines with high-speed networking to connect thousands of accelerators into a single, unified system. Take a look at the Helios. Our rack-scale AI platform powered by MI455X GPUs and EPYC “Venice” CPUs and designed for trillion-parameter training.

Building AI infrastructure at yotta-scale requires more than raw performance; it demands an open, modular rack design that can evolve across product generations, combining leadership compute engines with high-speed networking to connect thousands of accelerators into a single, unified system. Take a look at the Helios. Our rack-scale AI platform powered by MI455X GPUs and EPYC “Venice” CPUs and designed for trillion-parameter training.

AMD

20,832 Aufrufe • vor 6 Monaten

AMD CEO LISA SU JUST DOUBLED HER OWN CPU FORECAST IN 6 MONTHS In November, she told investors the CPU market would grow 18-20% per year through 2030. This morning she revised that number: "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." The driver: agents. Six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center. The new ratio is closer to 1 to 1. Su on CNBC: "Each of us could have 2 or 3 or even 5 or 10 agents doing work on our behalf. That means you need a lot more CPU compute."

AMD CEO LISA SU JUST DOUBLED HER OWN CPU FORECAST IN 6 MONTHS In November, she told investors the CPU market would grow 18-20% per year through 2030. This morning she revised that number: "That rate is doubling. The CPU market may be over $120 billion by 2030, growing at over 35%." The driver: agents. Six months ago AMD modeled 1 CPU for every 4 to 8 GPUs in an AI data center. The new ratio is closer to 1 to 1. Su on CNBC: "Each of us could have 2 or 3 or even 5 or 10 agents doing work on our behalf. That means you need a lot more CPU compute."

WOLF

88,438 Aufrufe • vor 2 Monaten

Introducing HOME STRETCH: PVRIS. In 2014, PVRIS released their debut album 'White Noise' to critical acclaim catapulting the New England-born alternative act into the mainstream music scene. But for PVRIS, the success didn't happen overnight. Join us for our third installment of HOME STRETCH, as we head back to Lowell, Massachusetts to learn more about the band's come up. Episode 1 airs tomorrow at 9:30am PT / 12:30pm ET: Episode 2 out Thursday, 4/10 at 9:30am PT / 12:30pm ET Episode 3 out Thursday, 4/17 at 9:30am PT / 12:30pm PT #PVRIS

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Live Nation

18,519 Aufrufe • vor 1 Jahr

We're up 800%+ and we're only in the 3rd inning. There will be red days ahead, which means BUY. Here's the top 10 stocks to buy on every SINGLE dip until I tell you when to sell: 1. Micron $MU 2. Western Digital Corp. $WDC 3. Seagate Technology $STX 4. GE Vernova $GEV 5. Lam Research Corp. $LRCX 6. Sandisk $SNDK 7. Advanced Micro Devices $AMD 8. Corning $GLW 9. Kopin $KOPN 10. Arista Networks $ANET Never miss a bull-run again - all my buy and sell signals in Discord @

We're up 800%+ and we're only in the 3rd inning. There will be red days ahead, which means BUY. Here's the top 10 stocks to buy on every SINGLE dip until I tell you when to sell: 1. Micron $MU 2. Western Digital Corp. $WDC 3. Seagate Technology $STX 4. GE Vernova $GEV 5. Lam Research Corp. $LRCX 6. Sandisk $SNDK 7. Advanced Micro Devices $AMD 8. Corning $GLW 9. Kopin $KOPN 10. Arista Networks $ANET Never miss a bull-run again - all my buy and sell signals in Discord @

StockWhale

290,399 Aufrufe • vor 2 Monaten

This is how you buy stocks that look like $MU, $SNDK, $RKLB, $AMD, $NBIS, $QCOM, $INTC, etc. right now. I teach you how to buy parabolic stocks the RIGHT way. The most important 3-minutes of your life that make and save millions of dollars: 1. Buy a very small position now. This gets rid of your FOMO and helps psychologically. 2. Buy when stocks reach the daily 62 / 79 EMA & 200 MA (needs to be in a clear uptrend). 3. I became a multi-millionaire because I buy LOW and trim HIGH. I followed these EXACT rules. 4. This is what I teach to my subscribers EVERYDAY. We buy LOW and trim HIGH. Follow my every move and you will retire in a few years.

This is how you buy stocks that look like $MU, $SNDK, $RKLB, $AMD, $NBIS, $QCOM, $INTC, etc. right now. I teach you how to buy parabolic stocks the RIGHT way. The most important 3-minutes of your life that make and save millions of dollars: 1. Buy a very small position now. This gets rid of your FOMO and helps psychologically. 2. Buy when stocks reach the daily 62 / 79 EMA & 200 MA (needs to be in a clear uptrend). 3. I became a multi-millionaire because I buy LOW and trim HIGH. I followed these EXACT rules. 4. This is what I teach to my subscribers EVERYDAY. We buy LOW and trim HIGH. Follow my every move and you will retire in a few years.

AskLivermore

456,190 Aufrufe • vor 2 Monaten

$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!

$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!

Mike

104,109 Aufrufe • vor 1 Monat

MY 5 BIGGEST TAKEAWAYS FROM $AMD ANALYST DAY 1. The pivot is official that AMD it’s building a platform with a goal is to sell full AI infrastructure: silicon, interconnect, software & racks that hyperscalers can deploy at scale. It’s chasing $NVDA vertical model but through an open ecosystem. 2. Data center is the company with Lisa guiding for ~35% CAGR, 57% gross margins & >$20 EPS by 2030. That requires DC biz to grow from ~$16B today to near $100B within five years which is a 6x run that only works if MI450 + Helios hit ramp targets & hyperscaler orders stack fast. 3. GPU is the upside but ROCm is the gatekeeper. The MI450 & yearly GPU cadence will define whether AMD becomes a real platform or stays a second source. Success hinges on ROCm feeling as stable and integrated as CUDA but partnerships with $META, $ORCL & OpenAI are the first real signs of ecosystem lift. 4. CPUs fund the fight with the server share near 40% gives AMD the profit engine it needs. As AI inference workloads push compute back toward CPUs, crossing 50% share does actually look realistic & that stable cash flow supports GPU & software expansion without breaking margins. 5. Michael Burry is wrong.

MY 5 BIGGEST TAKEAWAYS FROM $AMD ANALYST DAY 1. The pivot is official that AMD it’s building a platform with a goal is to sell full AI infrastructure: silicon, interconnect, software & racks that hyperscalers can deploy at scale. It’s chasing $NVDA vertical model but through an open ecosystem. 2. Data center is the company with Lisa guiding for ~35% CAGR, 57% gross margins & >$20 EPS by 2030. That requires DC biz to grow from ~$16B today to near $100B within five years which is a 6x run that only works if MI450 + Helios hit ramp targets & hyperscaler orders stack fast. 3. GPU is the upside but ROCm is the gatekeeper. The MI450 & yearly GPU cadence will define whether AMD becomes a real platform or stays a second source. Success hinges on ROCm feeling as stable and integrated as CUDA but partnerships with $META, $ORCL & OpenAI are the first real signs of ecosystem lift. 4. CPUs fund the fight with the server share near 40% gives AMD the profit engine it needs. As AI inference workloads push compute back toward CPUs, crossing 50% share does actually look realistic & that stable cash flow supports GPU & software expansion without breaking margins. 5. Michael Burry is wrong.

Shay Boloor

244,819 Aufrufe • vor 8 Monaten

"Tomorrowland presents: Atmosphere" is back on Friday, November 14 at Antwerp Expo. Prepare for a night of high-energy performances from some of the most influential names in the scene. The full line-up drops on Thursday, September 4. Pre-Sale starts on Tuesday September 9. General Sale starts on Thursday September 11. Register now for exclusive access to the Pre-Sale:

"Tomorrowland presents: Atmosphere" is back on Friday, November 14 at Antwerp Expo. Prepare for a night of high-energy performances from some of the most influential names in the scene. The full line-up drops on Thursday, September 4. Pre-Sale starts on Tuesday September 9. General Sale starts on Thursday September 11. Register now for exclusive access to the Pre-Sale:

Tomorrowland

15,674 Aufrufe • vor 1 Jahr

Crude is down more than 10% today. The Strait is open. Yesterday, Mark Newton CMT covered the technical setup behind crude and energy stocks, the S&P's 11-day recovery from a 100-day low to a 200-day high, and what the charts say about whether the rally holds into May. Keep watching for his full market roadmap, plus live analysis on $TSLA, $NVDA, $AMD, $DELL, $PLTR, $BABA, $MU, $AMAT, and more:

Crude is down more than 10% today. The Strait is open. Yesterday, Mark Newton CMT covered the technical setup behind crude and energy stocks, the S&P's 11-day recovery from a 100-day low to a 200-day high, and what the charts say about whether the rally holds into May. Keep watching for his full market roadmap, plus live analysis on $TSLA, $NVDA, $AMD, $DELL, $PLTR, $BABA, $MU, $AMAT, and more:

Fundstrat Direct

15,616 Aufrufe • vor 2 Monaten

My interview with Greg Brockman (Greg Brockman), Cofounder and President of OpenAI. 0:00 Intro 1:06 Scaling Sora 2:18 Are transformer models the future? 4:29 Building with AMD 5:50 Other kinds of compute 7:28 Bottlenecks 9:17 Deciding where to invest 11:38 Decoupling of the internet 16:29 Why ChatGPT is different 17:59 Proactive AI 19:39 Compressing Intelligence 20:48 ChatGPT’s Longest Thought Ever 21:13 Sora 2 29:47 Is my job in danger? 31:51 Building on top of OpenAI 34:11 Humans in the loop 35:36 Fully generated software 39:25 Agentic Commerce Protocol 40:41 What does 2026 look like? 42:43 AGI

My interview with Greg Brockman (Greg Brockman), Cofounder and President of OpenAI. 0:00 Intro 1:06 Scaling Sora 2:18 Are transformer models the future? 4:29 Building with AMD 5:50 Other kinds of compute 7:28 Bottlenecks 9:17 Deciding where to invest 11:38 Decoupling of the internet 16:29 Why ChatGPT is different 17:59 Proactive AI 19:39 Compressing Intelligence 20:48 ChatGPT’s Longest Thought Ever 21:13 Sora 2 29:47 Is my job in danger? 31:51 Building on top of OpenAI 34:11 Humans in the loop 35:36 Fully generated software 39:25 Agentic Commerce Protocol 40:41 What does 2026 look like? 42:43 AGI

Matthew Berman

45,086 Aufrufe • vor 9 Monaten

November 4, 1984 #49ers legend Joe Montana throws a career-high four interceptions, but guides his team to 16 unanswered second-half points in a 23-17 comeback win over the #Bengals at Candlestick. Joe Cool hits Freddie Solomon for the game-winning touchdown with 1:39 remaining to move the Niners' record to 9-1. #FTTB

November 4, 1984 #49ers legend Joe Montana throws a career-high four interceptions, but guides his team to 16 unanswered second-half points in a 23-17 comeback win over the #Bengals at Candlestick. Joe Cool hits Freddie Solomon for the game-winning touchdown with 1:39 remaining to move the Niners' record to 9-1. #FTTB

Kevin Gallagher

13,709 Aufrufe • vor 8 Monaten

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026

$AMD's heading to $5T MC LT| Lowest $/M tokens 🧵 The real reason why Institutions are FOMOing into AMD while other Semi stocks are underperforming ($NVDA $AVGO) Not Financial Advice! DYOR! Under Dr. Lisa Su’s leadership, AMD has transformed from a distant challenger into a formidable force in AI infrastructure, delivering the industry’s most compelling TCO story for high-volume inference. Her clear vision open ecosystems, aggressive annual roadmaps, rack-scale innovation, and relentless focus on tokens-per-dollar has positioned AMD’s Helios racks as the go-to solution for hyperscalers and AI natives struggling with exploding token costs, collapsing the cost down to $0.0003-$0.0005/M tokens. I will link various threads on this analysis to supply chain and wafer ratio if you are interested in understanding the full picture. In the last 3-4 months, explosive Agentic AI demand significantly increased Inference demand for Agentic AI models with 5-10 agents. If you are a listener of CNBC or Bloomberg, u should know enterprises and companies are complaining abt cost of token, and how it starts to spike up way too much to make sense. The fact that most data center today are run by $NVDA Chips, where the cost is way too high for Training or Inference. 1. Token cost Here are some quick comp, so u understand why $META OpenAI Anthropic $MSFT $AMZN Softbank $GOOGL and many more small to medium AI Natives are buying AMD CPUs and GPUs as much as they want, or pretty much AMD chips are sold out for the next 3-5 years. Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens 2. Why Hyperscalers and AI Natives Are Choosing AMD Token consumption (especially Agentic) is outpacing even NVIDIA’s efficiency gains, making diversification mandatory for economic viability. Massive deals reflect this reality like $META, OpenAI, $MSFT, Softbank, $AMZN, Oracle, LumaAI, G42... Dr. Lisa Su’s Vision in Action: Since taking the helm, Su has driven AMD’s turnaround with disciplined execution, annual GPU cadence (MI300 → MI350 → MI400), full-stack software (ROCm 7), open ecosystems (UALink, OCP designs), and customer-centric rack-scale solutions like Helios. Her emphasis on “tokens per dollar” and TCO has turned AMD into the pragmatic choice for sustainable AI scaling. Power/Energy Efficiency: ~Helios Rack-level is estimated at 120kW-140kW with 50% more HBM4 where Inference and Training cost matter ~Rubin Rack-Level is estimated at 160kW-230kw AMD Helios shines in owned TCO, memory density, and energy flexibility at hyperscale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B 3. Superior CPUs to pair with GPUs on massive scale 5-10-20GW Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. Conclusion: NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always-on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. Not Financial Advice! DYOR! Video source: Microsoft Build 2026

Mike

145,550 Aufrufe • vor 1 Monat

$AMD is ready to break $1 Trillion MC| $TSM 2nm🧵 TLDR FY 2026(Excluding China AI Revenue) AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 The semiconductor industry is at a pivotal juncture, with advanced process nodes like TSMC's 2nm technology becoming the battleground for leadership in artificial intelligence and high-performance computing (HPC). Amid this landscape, AMD stands poised to secure early production and higher allocation of its Venice (EPYC ) and MI450 (Instinct GPUs) on TSMC's 2nm process. This strategic advantage is not merely a product of timing but a culmination of a robust partnership, market demand, technical superiority, and geopolitical dynamics. The AI and HPC markets are experiencing unprecedented growth, with inference workloads projected to constitute 80-90% of AI compute by 2030. AMD's EPYC processors and Instinct GPUs are uniquely positioned to capitalize on this trend, particularly given the demand from hyperscalers such as OpenAI , $META , $MSFT, $AMZN, and $ORCL. With $TSM starting 2nm Mass Production in Taiwan is ensuring AMD to meet FY2026 $70B to $100B revenue, driven by non-GAAP net income of $18B to $25B highlights the scale of this opportunity, starkly contrasting with analyst revenue consensus of $39-$45B. This discrepancy arises from analysts' failure to account for major orders, notably from OpenAI(Today SoftBank secured OpenAI a massive cash balance of $55-$62B).OpenAI is raising $100B, so this left $77B from UAE, Saudi, $MSFT, and others. $AMD is on track to receive higher allocation of EPYC Venice and Mi450 in 2026. AMD's acquisition of Xilinx has significantly strengthened its position in AI inference, particularly through adaptive computing technologies like FPGA-based AI Engines. The upcoming Zen 6 "Venice" generation (on TSMC 2nm, launching with MI450 in 2026) promises ~1.7× performance uplift, enhanced vector/AI capabilities, greater thread density, and open firmware innovations positioning EPYC to maintain its inference leadership while powering massive hybrid AI superclusters. TSMC's Fab 22 in Kaohsiung, Taiwan, is now the epicenter of 2nm mass production, a earlier strategic move to meet soaring demand from $AMD and $AAPL. Early production slots are typically reserved for customers with the highest revenue potential and strategic importance. AMD's early tape-out of Venice and the MI450's role as the first AMD GPU on 2nm place it at the forefront of this allocation. The 2nm process offers 10-15% higher performance or 25-30% lower power use compared to 3nm, a critical advantage for AI and HPC applications(TSMC claimed) Moreover, TSMC's recent 20% yield improvement in Versal production, as mentioned in related discussions, indicates efficient scaling. Higher yields translate to more chips produced per wafer, reducing costs and increasing allocation for key customers like AMD. This efficiency is particularly important given the aggressive timelines of customers like OpenAI, who require rapid scaling to meet their computational needs. The reopening of the China market adds another layer of demand pressure. Vendors and hyperscalers are begging for allocation of AMD's MI308X, MI300X, and MI355X, and the 2nm capacity will be critical to meet this need. TSMC's early production of 2nm ensures AMD can capitalize on this opportunity, securing higher allocation to fulfill these orders. Dr. Lisa Su's emphasis on disciplined supply chain planning for multiple gigawatt-scale customers, such as OpenAI, demonstrates AMD's readiness to scale. TSMC's confidence in AMD's ability to absorb this capacity is evident in the early 2nm production allocation. This discipline is particularly important in a market where demand outstrips supply by 10-12x. TSMC's competitors, such as Samsung and Intel, are still in the early stages of their 2nm and equivalent processes. Samsung's 2nm GAA transistors and Intel's 18A process are not yet in mass production, giving TSMC and AMD a first-mover advantage. Nvidia's acquisition of Groq Inc. is a defensive move to diversify into inference, but it does not immediately address the 2nm gap. AMD EPYC Venice and future Gen are already ahead of lowest cost for Inference along with MI450 has TCO of $0.65 to $1.00 per million inference tokens, significantly lower than Nvidia's Rubik (H2 2026) at $0.70 to $1.20 and Broadcom's XPU (2027-2029) at $0.70 to $1.30. Additionally, the MI450's TDP is estimated at 1000-1800W, compared to Nvidia's 2300-3600W (Ultra), reducing operational costs and energy consumption(TSMC 2nm vs TSMC 3nm). The MI450 features 432GB of HBM4 memory and 19.6 TB/s bandwidth, surpassing Nvidia's Rubik (288GB HBM4, 16 TB/s) and Broadcom's XPU (192/256GB HBM4, 7 TB/s est). This enhanced memory and bandwidth capacity is essential for handling the complex, data-intensive workloads of large language models and other AI applications. AMD's full-stack vision, combining EPYC hosts with Instinct accelerators, offers the lowest total cost of ownership (TCO) and thermal design power (TDP). This synergy is unbeatable for both training and inference, further justifying TSMC's prioritization. The 2nm process amplifies these advantages, ensuring AMD can maintain its competitive edge over rivals like Nvidia, whose Rubin GPUs are still on N3P (a 3nm derivative). Today, TSMC just secured $AMD to join the top 10 largest companies in the world as it begins 2nm mass production in Taiwan. AMD and Apple are to receive highest allocation. The long-standing partnership with TSMC, massive demand from hyperscalers, technical advantages of 2nm, and disciplined supply chain planning all point to AMD's favored position. The 2nm process's early mass production at Fab 22, combined with AMD's revenue potential and competitive edge, justifies TSMC's prioritization. This allocation is critical for AMD to meet aggressive demand, capture market share, and solidify its position as a leader in AI and HPC, especially in the inference-dominated future. Dr. Lisa Su "We will multiple customers/hyperscalers at GW scale" Not Financial Advice!

$AMD is ready to break $1 Trillion MC| $TSM 2nm🧵 TLDR FY 2026(Excluding China AI Revenue) AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 The semiconductor industry is at a pivotal juncture, with advanced process nodes like TSMC's 2nm technology becoming the battleground for leadership in artificial intelligence and high-performance computing (HPC). Amid this landscape, AMD stands poised to secure early production and higher allocation of its Venice (EPYC ) and MI450 (Instinct GPUs) on TSMC's 2nm process. This strategic advantage is not merely a product of timing but a culmination of a robust partnership, market demand, technical superiority, and geopolitical dynamics. The AI and HPC markets are experiencing unprecedented growth, with inference workloads projected to constitute 80-90% of AI compute by 2030. AMD's EPYC processors and Instinct GPUs are uniquely positioned to capitalize on this trend, particularly given the demand from hyperscalers such as OpenAI , $META , $MSFT, $AMZN, and $ORCL. With $TSM starting 2nm Mass Production in Taiwan is ensuring AMD to meet FY2026 $70B to $100B revenue, driven by non-GAAP net income of $18B to $25B highlights the scale of this opportunity, starkly contrasting with analyst revenue consensus of $39-$45B. This discrepancy arises from analysts' failure to account for major orders, notably from OpenAI(Today SoftBank secured OpenAI a massive cash balance of $55-$62B).OpenAI is raising $100B, so this left $77B from UAE, Saudi, $MSFT, and others. $AMD is on track to receive higher allocation of EPYC Venice and Mi450 in 2026. AMD's acquisition of Xilinx has significantly strengthened its position in AI inference, particularly through adaptive computing technologies like FPGA-based AI Engines. The upcoming Zen 6 "Venice" generation (on TSMC 2nm, launching with MI450 in 2026) promises ~1.7× performance uplift, enhanced vector/AI capabilities, greater thread density, and open firmware innovations positioning EPYC to maintain its inference leadership while powering massive hybrid AI superclusters. TSMC's Fab 22 in Kaohsiung, Taiwan, is now the epicenter of 2nm mass production, a earlier strategic move to meet soaring demand from $AMD and $AAPL. Early production slots are typically reserved for customers with the highest revenue potential and strategic importance. AMD's early tape-out of Venice and the MI450's role as the first AMD GPU on 2nm place it at the forefront of this allocation. The 2nm process offers 10-15% higher performance or 25-30% lower power use compared to 3nm, a critical advantage for AI and HPC applications(TSMC claimed) Moreover, TSMC's recent 20% yield improvement in Versal production, as mentioned in related discussions, indicates efficient scaling. Higher yields translate to more chips produced per wafer, reducing costs and increasing allocation for key customers like AMD. This efficiency is particularly important given the aggressive timelines of customers like OpenAI, who require rapid scaling to meet their computational needs. The reopening of the China market adds another layer of demand pressure. Vendors and hyperscalers are begging for allocation of AMD's MI308X, MI300X, and MI355X, and the 2nm capacity will be critical to meet this need. TSMC's early production of 2nm ensures AMD can capitalize on this opportunity, securing higher allocation to fulfill these orders. Dr. Lisa Su's emphasis on disciplined supply chain planning for multiple gigawatt-scale customers, such as OpenAI, demonstrates AMD's readiness to scale. TSMC's confidence in AMD's ability to absorb this capacity is evident in the early 2nm production allocation. This discipline is particularly important in a market where demand outstrips supply by 10-12x. TSMC's competitors, such as Samsung and Intel, are still in the early stages of their 2nm and equivalent processes. Samsung's 2nm GAA transistors and Intel's 18A process are not yet in mass production, giving TSMC and AMD a first-mover advantage. Nvidia's acquisition of Groq Inc. is a defensive move to diversify into inference, but it does not immediately address the 2nm gap. AMD EPYC Venice and future Gen are already ahead of lowest cost for Inference along with MI450 has TCO of $0.65 to $1.00 per million inference tokens, significantly lower than Nvidia's Rubik (H2 2026) at $0.70 to $1.20 and Broadcom's XPU (2027-2029) at $0.70 to $1.30. Additionally, the MI450's TDP is estimated at 1000-1800W, compared to Nvidia's 2300-3600W (Ultra), reducing operational costs and energy consumption(TSMC 2nm vs TSMC 3nm). The MI450 features 432GB of HBM4 memory and 19.6 TB/s bandwidth, surpassing Nvidia's Rubik (288GB HBM4, 16 TB/s) and Broadcom's XPU (192/256GB HBM4, 7 TB/s est). This enhanced memory and bandwidth capacity is essential for handling the complex, data-intensive workloads of large language models and other AI applications. AMD's full-stack vision, combining EPYC hosts with Instinct accelerators, offers the lowest total cost of ownership (TCO) and thermal design power (TDP). This synergy is unbeatable for both training and inference, further justifying TSMC's prioritization. The 2nm process amplifies these advantages, ensuring AMD can maintain its competitive edge over rivals like Nvidia, whose Rubin GPUs are still on N3P (a 3nm derivative). Today, TSMC just secured $AMD to join the top 10 largest companies in the world as it begins 2nm mass production in Taiwan. AMD and Apple are to receive highest allocation. The long-standing partnership with TSMC, massive demand from hyperscalers, technical advantages of 2nm, and disciplined supply chain planning all point to AMD's favored position. The 2nm process's early mass production at Fab 22, combined with AMD's revenue potential and competitive edge, justifies TSMC's prioritization. This allocation is critical for AMD to meet aggressive demand, capture market share, and solidify its position as a leader in AI and HPC, especially in the inference-dominated future. Dr. Lisa Su "We will multiple customers/hyperscalers at GW scale" Not Financial Advice!

Mike

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