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⚡⚪ Power up your game & AI performance with the AORUS GeForce RTX™ 5090 MASTER ICE 32G! 💥 ❄️ Wrapped in a sleek white, it’s the perfect choice for a clean build. 🤍✨ 🟠 Powered by the NVIDIA Blackwell architecture and DLSS 4 🟠 WINDFORCE cooling system with the...

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AORUS

422,823 subscribers

13,878 Aufrufe • vor 1 Jahr •via X (Twitter)

GamingWissenschaft & TechnologieKunst
LIVE

Anya Rossi• Live Now

Private livecam show

9 Kommentare

Profilbild von ashley
ashleyvor 1 Jahr

Haven’t seen these available anywhere 😭

Profilbild von RedDeer.Games
RedDeer.Gamesvor 2 Jahren

New is better Together with @mgpstudios we present you... 🟥 NECKBREAK 🟥 Stylish, #cyberpunk, #retro first-person shooter is available now! 🔥 Play it on ⤵️ #NintendoSwitch => #Xbox => #indiegames #release

Profilbild von ⚔️🔥THE RAFCAVE🔥⚔️
⚔️🔥THE RAFCAVE🔥⚔️vor 1 Jahr

Not available

Profilbild von Anakwah
Anakwahvor 1 Jahr

Need one for free

Profilbild von チョコミント
チョコミントvor 1 Jahr

Stop posting this shit when people can't even fucking buy it.

Profilbild von Proxy_2022
Proxy_2022vor 1 Jahr

Ok, I sell a kidney and I go back...

Profilbild von Empowered PC
Empowered PCvor 1 Jahr

NGL I need in my all white build RN

Profilbild von Henrique Campos Carlos Pereira
Henrique Campos Carlos Pereiravor 1 Jahr

Best GPU ever.👏

Profilbild von Madhatterni
Madhatternivor 1 Jahr

shouldnt even be in the box when it open. Just to highlight the issue with availability

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Jensen Huang just BROKE the most important rule in the industry. And it explains why Nvidia controls 95% of the AI chip market. Last night at CES, he unveiled Vera Rubin - the new AI supercomputer that's shipping right now. Full production started weeks ago. But here's the part that made every semiconductor engineer in the room go crazy: Reuben GPU is 5x faster than Blackwell. But only has 1.6x the transistors. That should be physically impossible. Moore's Law says you get maybe 25% more performance per transistor generation. Jensen just delivered 300%. How? He BROKE the most sacred rule in chip design. The rule every company follows: "Never redesign more than 1-2 chips per generation." Nvidia redesigned all six chips simultaneously. Vera CPU. Reuben GPU. Connect X9 networking. Bluefield 4 DPU. MVLink switches. Spectrum X Ethernet. Every. Single. Component. From scratch. He calls it "extreme co-design." The industry calls it insane. One rack now moves 240 terabytes per second. That's TWICE the entire global internet bandwidth. In a single rack. And it runs on 45°C water - no chillers needed. Which saves 6% of global data center power. But the real story isn't the hardware... It's what they're doing with it. Nvidia just open-sourced Alpha Mayo. The world's first reasoning autonomous vehicle AI. Mercedes-Benz CLA launches with it in Q1. Europe Q2. Asia by year-end. Not a concept car. Not a limited release. Full production vehicles. And the AI will even explain its reasoning out loud. "I'm slowing down because the truck ahead is braking and there's a cyclist merging." It thinks. Then tells you what it's thinking. Then executes. Jensen drove it through San Francisco for an hour yesterday. No hands. No interventions. Through heavy Sunday traffic. The whole thing is open source now. Every line of training code. Every data source. The entire stack. But why would Nvidia give this away? Because they learned something from the last year: Open models activated the entire world. DeepSeek R1 proved open source can hit the frontier. Downloads exploded. Every country, every startup, every researcher can now build AI. And they all need Nvidia hardware to train it. That's the strategy. Give away the recipes. Sell the kitchen. The partnerships tell you where this is going: Siemens is integrating Nvidia into every industrial design tool. Cadence and Synopsys are rebuilding chip design around Nvidia. Palantir, ServiceNow, Snowflake - their entire platforms now run on Nvidia's agentic AI stack. This isn't just selling chips anymore. Nvidia is rebuilding the entire computing stack. From design to manufacturing to deployment. Every layer of the trillion-dollar AI infrastructure buildout runs through them. And now they're 18 months ahead of everyone else. Again. The competition is still trying to match Blackwell. Nvidia's already shipping the thing that makes Blackwell look slow. What do you think - is anyone catching them? The only company capable of this might be Google.

Ricardo

580,077 Aufrufe • vor 7 Monaten

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).
9:00

$MU $SNDK $LITE $VRT NVIDIA and Groq: 2nd and 3rd Order Strategic Infrastructure Effects and Market Implications Public reporting indicates NVIDIA has agreed to acquire Groq for approximately $20,000,000,000 in cash, while excluding Groq’s nascent cloud business from the transaction perimeter. The reported carve-out materially constrains the immediate, direct linkage from the acquisition to incremental, NVIDIA-controlled data center capacity build-out because GroqCloud appears to be the principal channel through which Groq hardware is currently monetized at scale as a service. The infrastructure-market implications therefore depend primarily on post-close product strategy: whether NVIDIA (1) commercializes Groq silicon as a distinct inference product line and drives broad deployment through OEM/ODM channels and partners, (2) uses the acquisition mainly to absorb IP and talent while de-emphasizing standalone Groq hardware volumes, or (3) uses Groq technology to reshape NVIDIA’s own inference systems and networking roadmaps. The dominant transmission mechanism into memory, networking, and facility infrastructure markets is the degree to which NVIDIA shifts incremental inference deployments away from GPU architectures that are tightly coupled to external high-bandwidth memory (HBM) and toward Groq’s current architecture, which emphasizes large on-chip SRAM, deterministic compiler-scheduled execution, and direct chip-to-chip connectivity. Independent and company-published materials describe Groq’s current-generation approach as having no external memory, keeping weights and KV cache on-chip during processing, and requiring model sharding across multiple chips due to limited on-chip SRAM per device. That architectural choice is directionally HBM-negative on a per-accelerator basis and ambiguous for DRAM, NAND, networking, power, and cooling on a per-token basis because the design can reduce memory wall losses and tail-latency overhead while potentially increasing the number of chips and interconnect endpoints required to serve large models and long-context workloads. HBM implications are the most mechanically straightforward but should be framed as second-derivative rather than absolute. If Groq-class inference silicon meaningfully displaces NVIDIA GPU-based inference deployments, incremental HBM bit demand tied to inference growth could be reduced relative to a GPU-only baseline because Groq’s current approach does not appear to attach HBM stacks to each accelerator. However, current market structure suggests HBM remains supply-constrained and is being pulled by multiple vectors including continued GPU training scale and high-capacity inference configurations, with leading suppliers signaling tight conditions extending beyond 2026. In that environment, reduced inference-driven HBM intensity could primarily reallocate scarce HBM supply toward higher-end training and premium inference GPUs rather than creating an outright volume collapse, preserving high utilization of HBM capacity while potentially affecting the slope of pricing power and capacity expansion urgency over a multi-year horizon. The key downside scenario for the HBM complex would be a durable architectural bifurcation where “good-enough” inference shifts disproportionately to HBM-less ASICs across a broad swath of deployments (latency-sensitive, batch-1, cost-per-token optimized), while training remains GPU-HBM dominated; such a split would reduce the portion of future inference compute that naturally monetizes through HBM content and could compress the incremental HBM-per-AI-dollar ratio. The key upside/neutral scenario for HBM is that the supply chain remains fully allocated regardless, with NVIDIA using any “freed” HBM to ship more high-end GPUs into training and long-context inference, especially as roadmaps increase HBM per GPU, sustaining robust aggregate bit demand even if inference becomes more heterogeneous. Conventional DRAM implications split into 2 channels: (1) DRAM wafer capacity diversion into HBM and (2) DDR content per server in AI clusters. Supplier commentary indicates that AI-driven memory demand is supporting elevated DRAM markets more broadly, and HBM production is resource-intensive versus conventional DRAM, tightening supply for DDR products in parallel. A meaningful NVIDIA pivot to an inference architecture that reduces HBM dependence could, at the margin, ease the most acute HBM-driven bottlenecks and allow memory manufacturers more flexibility in balancing DRAM mix, which could be modestly DDR-positive on the supply side (less crowding-out) even if it is DDR-neutral or slightly negative on the demand side (if per-node CPU/DDR requirements decline due to more efficient accelerator utilization). The dominant practical outcome is likely that DDR demand remains supported by broad AI server proliferation and increasing memory footprints at the system level (CPUs, networking stacks, caching layers, retrieval-augmented pipelines), while HBM remains the premium profit pool; therefore, any HBM displacement that increases total server volumes could indirectly keep DDR demand resilient even if DDR per accelerator is not rising materially. NAND flash implications are comparatively indirect and volume-driven rather than architecture-driven. Inference clusters require SSD capacity for model storage, container images, logging, and increasingly for fast local retrieval indices and embedding stores, but the storage footprint per unit of compute is typically smaller than in training pipelines that stage large datasets and checkpoints. If NVIDIA uses Groq to lower inference cost and latency enough to expand the total number of inference deployment locations (regional colocation, enterprise on-prem, sovereign footprints), aggregate SSD attach could rise through geographic fragmentation and replication of model artifacts across more sites, even if per-site storage is modest. The NAND effect is therefore likely to be demand-broadening and mix-positive (datacenter SSDs) but not a primary swing factor versus the macro AI capex cycle and consumer/device cycles. Hard disk drive (HDD) markets should see negligible direct sensitivity because nearline HDD demand is driven by bulk storage and cloud archiving economics, while inference acceleration choices primarily reshape compute and network layers; any HDD benefit would be a tertiary function of overall data center square footage expansion rather than a direct consequence of Groq silicon displacing GPUs. Optical networking implications require separating (1) intra-cluster back-end fabrics that connect accelerators and (2) front-end / data center interconnect (DCI) that connects sites and regions. Groq’s own positioning and third-party reporting suggest scaling beyond a single node or rack relies on high-bandwidth fabrics and, in some described configurations, optical interconnect scaling across hundreds of chips. If NVIDIA commercializes Groq at scale, 2 offsetting forces emerge: lower cost-per-token and improved latency could expand inference throughput and drive more east-west traffic, increasing demand for high-speed switching and optics; conversely, if Groq delivers materially higher utilization and tokens per unit of network bandwidth for certain workloads, the network required per served token could decline. Public NVIDIA materials already indicate an aggressive photonics roadmap aimed at scaling AI factories, including co-packaged optics (CPO) switches and explicit collaboration with Coherent and Lumentum in the silicon photonics supply chain. That linkage is important because it suggests that, independent of Groq, NVIDIA is already pushing optics integration deeper into the switch package to reduce power and increase resiliency; Groq increases the strategic incentive to reduce network power and latency if inference becomes even more distributed and latency-sensitive. For Lumentum and Coherent specifically, the net implication is less about “more optics versus fewer optics” and more about a shift in optics form factor and value capture. Co-packaged optics can reduce reliance on pluggable transceivers in some switch architectures while increasing demand for integrated photonic engines, lasers, fiber attach, packaging processes, and component-level supply. NVIDIA’s own announcements explicitly position Coherent and Lumentum as collaborators in creating the integrated silicon/optics process and supply chain for photonics switches. If Groq accelerates the transition to very large-scale fabrics (more endpoints, higher port speeds, tighter power envelopes), that tends to pull forward CPO adoption and amplifies demand for the underlying photonics components even if the conventional pluggable module TAM is structurally pressured over time. If Groq instead pushes inference toward smaller, more localized pods (closer to users, more regional colocation), that can be optics-positive for DCI and metro connectivity because more sites must be interconnected at high bandwidth with low latency, favoring coherent optics and high-speed interconnect between facilities. The principal risk for optics suppliers is timing and margin structure: a faster move to NVIDIA-driven integrated photonics could concentrate bargaining power and compress margins for commoditized transceiver modules while favoring suppliers with differentiated lasers, integration capability, and qualification depth in NVIDIA’s CPO ecosystem. AEC and copper interconnect implications hinge on whether Groq deployment increases the density of short-reach links inside racks and rows. High-speed copper remains structurally advantaged at very short distances on cost, power, and serviceability, but reaches become constrained as lane speeds and aggregate bandwidth rise, creating a role for active electrical cables (AECs), retimers, and signal-conditioning silicon. Credo explicitly positions its AEC products as enabling reliable lossless 800G connectivity for AI clusters, and the company has highlighted participation at NVIDIA GTC with content focused on extending PCIe/CXL using AECs, indicating relevance to next-generation system topologies that require longer reach and higher signal integrity than passive copper can deliver. If NVIDIA turns Groq into a widely deployed inference card or chassis product, the likely near-term effect is AEC-positive because (1) more inference throughput tends to increase top-of-rack connectivity requirements, (2) distributing inference across more racks and sites increases short-reach links per unit of delivered service, and (3) PCIe-attached accelerator architectures tend to require robust signal conditioning as systems move to PCIe 6.x and beyond. Groq workshop materials explicitly reference GroqCard and GroqNode form factors, reinforcing that PCIe-attached deployment has been central to Groq’s current packaging strategy. The main countervailing risk is that Groq’s deterministic chip-to-chip fabric could be implemented primarily through backplanes and direct board-level connectivity that reduces the need for merchant AECs inside the box; in that case, incremental AEC demand would concentrate more in rack-to-switch and node-to-fabric links rather than within-chassis chip fabrics. Astera Labs implications are connectivity-architecture sensitive and, on balance, skew positive if NVIDIA increases heterogeneity and disaggregation in AI systems. NVIDIA has publicly positioned NVLink Fusion as a pathway for partners to build semi-custom AI infrastructure and has explicitly identified Astera Labs as a partner in that ecosystem, with Astera describing NVLink-related solutions expanding its connectivity platform across PCIe, CXL, and Ethernet plus fleet observability software. A Groq acquisition increases the probability that NVIDIA offers a broader menu of accelerators (training GPUs, inference-focused ASICs) and therefore increases the importance of scalable, high-reliability connectivity, retiming, switching, and telemetry across mixed topologies. If Groq silicon remains PCIe-attached in many deployments, PCIe 6.x retimers/switches and active cable modules become more central, aligning with Astera’s core portfolio. If NVIDIA instead integrates Groq concepts into scale-up fabrics (NVLink-like domains) or uses Groq to expand into inference “appliances” that must be rapidly deployed in colocation environments, the need for standard-compliant, serviceable connectivity with strong RAS/telemetry increases, again aligning with Astera’s positioning. Power equipment and cooling implications for Vertiv and adjacent suppliers should be viewed through the lens of rack power density, cooling modality (air vs liquid), and site deployment model (hyperscale campuses vs distributed colocation/enterprise). Groq claims its LPU and rack designs are “air-cooled by design” and require no complex cooling and power infrastructure, and third-party reporting has described Groq’s approach as relying on parallelism across many lower-power units rather than extreme per-chip performance. If NVIDIA scales Groq as a mainstream inference platform, the mix of data center cooling spend could shift modestly away from the highest-density liquid-cooled racks toward more air-cooled or hybrid deployments, particularly for inference pods placed in existing facilities that cannot easily retrofit for very high rack heat flux. That would be a mix headwind for suppliers most levered exclusively to high-end liquid cooling attachments per rack, but it is not necessarily a volume headwind for Vertiv given the company’s broad exposure to both power and cooling infrastructure and the likelihood that total AI deployment locations expand. Vertiv’s own industry commentary emphasizes that AI racks require higher power-density UPS, batteries, power distribution equipment, and switchgear capable of handling rapid load transients, and that hybrid cooling systems will evolve across deployment environments. Those statements align with a world where inference growth increases the count of powered racks and raises the operational complexity of power delivery even if per-rack density is lower than the most extreme training clusters. The most material infrastructure impact may occur outside the rack and upstream of the data hall: grid interconnects, substations, transformers, switchgear, generators, and utility-scale generation additions. Recent regulatory actions in the U.S. highlight that projected data center demand is already driving large planned increases in electricity generation capacity, underscoring that power availability is a binding constraint. In that context, an inference architecture that lowers joules per token could reduce the power required per unit of inference delivered, but it can also accelerate demand by lowering cost and improving latency, increasing the total volume of inference served (a classic rebound effect). The net outcome is likely continued, elevated demand for power infrastructure even if efficiency improves, with the key swing factor being whether AI capex remains on a multi-year growth trajectory or enters a digestion phase. Other data center infrastructure implications include server/ODM mix, facility design standardization, and networking architecture choices. If NVIDIA positions Groq-based inference as a broadly distributable “standard server + accelerator” solution rather than as an integrated, liquid-cooled rack like GB200 NVL72, spend could shift toward more conventional air-cooled server designs, higher unit volumes of mainstream racks, and faster deployment in colocation footprints, increasing demand for modular power rooms, busways, and rapidly deployable cooling solutions. If NVIDIA instead integrates Groq into its “AI factory” paradigm, the primary effect is likely acceleration of dense back-end fabric build-outs and a faster push toward photonics switching, increasing demand for fiber plant, connectors, and integrated optics supply chains while potentially compressing the lifecycle of transitional architectures based on pluggable optics and mid-reach copper. NVIDIA’s stated roadmap toward co-packaged optics and silicon photonics switches is already oriented toward scaling to very large GPU counts; adding a high-end inference ASIC increases the strategic importance of power-efficient, low-latency fabrics because inference economics become increasingly sensitive to network overhead as compute cost declines. Across the covered segments, the most defensible base case is limited near-term dislocation and a medium-term increase in uncertainty around memory intensity per unit of inference growth. HBM faces the clearest relative risk from an HBM-less inference platform, but supply tightness and GPU training roadmaps reduce the probability of an absolute demand shock over the next 12–24 months. Optical, AEC/copper, and power/cooling are more likely to remain volume-supported because they scale with endpoint count, deployment fragmentation, and total data center footprint, and those tend to rise when inference becomes cheaper and more widely deployed. The highest-conviction second-order effect is a shift in infrastructure mix: incrementally more distributed inference deployments (favoring colocation power/cooling standardization, DCI optics, and serviceable short-reach interconnect) and a gradual migration from pluggable optics toward integrated photonics in back-end fabrics (favoring suppliers positioned in the CPO ecosystem).

TheValueist

76,179 Aufrufe • vor 7 Monaten

What's the Big Deal with DeepSeek in AI? Here's why DeepSeek is making everyone take notice: 1. Super Smart on a Budget: DeepSeek showed you can make awesome AI without breaking the bank. Their latest model, DeepSeek-V3, was trained for only about $10 million, which is a lot less than the usual big bucks spent on AI, like the rumored $78 million for some of OpenAI's models. They did this in just two months with fewer fancy computers. 2. Open for Everyone: DeepSeek isn't keeping their tech a secret. They've made it open-source, meaning anyone can use, tweak, and learn from it. It's like they're saying, "Come join the party!" 3. Beating the Big Names: DeepSeek-V3 has done better than some top dogs from companies like OpenAI and Google in solving puzzles, math, and coding. This proves you can get great AI results without spending a fortune. 4. Challenging NVIDIA: NVIDIA's chips are usually the choice for AI because they're really powerful. But since DeepSeek did so well with less expensive chips, it might make people think twice about always going for NVIDIA's priciest options. 5. The DeepSeek Crew: The team at DeepSeek is young and smart, mostly from top Chinese schools, with brains in physics, math, and computer science. They learned AI in about six months by themselves! They use first principle thinking, which means they break down problems to the basics and build from there. This has helped them come up with cool new ways to do AI. 6. Changing AI for Good: DeepSeek is showing that AI can be cheaper and more open to everyone. They're changing how we think AI should be made and shared, which could shake up the whole AI world. So, as we watch DeepSeek, it's clear they're not just another player; they're changing the rules of the game. I predicted that this would be a make or break year for all the massive investments made in AI by American VC's. A few weeks later, DeepSeek happens! Watch the rest of my predictions in my 2025 outlook video . Link in replies #AIInnovation #DeepSeek #NVIDIA #OpenAI #TechDisruption
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What's the Big Deal with DeepSeek in AI? Here's why DeepSeek is making everyone take notice: 1. Super Smart on a Budget: DeepSeek showed you can make awesome AI without breaking the bank. Their latest model, DeepSeek-V3, was trained for only about $10 million, which is a lot less than the usual big bucks spent on AI, like the rumored $78 million for some of OpenAI's models. They did this in just two months with fewer fancy computers. 2. Open for Everyone: DeepSeek isn't keeping their tech a secret. They've made it open-source, meaning anyone can use, tweak, and learn from it. It's like they're saying, "Come join the party!" 3. Beating the Big Names: DeepSeek-V3 has done better than some top dogs from companies like OpenAI and Google in solving puzzles, math, and coding. This proves you can get great AI results without spending a fortune. 4. Challenging NVIDIA: NVIDIA's chips are usually the choice for AI because they're really powerful. But since DeepSeek did so well with less expensive chips, it might make people think twice about always going for NVIDIA's priciest options. 5. The DeepSeek Crew: The team at DeepSeek is young and smart, mostly from top Chinese schools, with brains in physics, math, and computer science. They learned AI in about six months by themselves! They use first principle thinking, which means they break down problems to the basics and build from there. This has helped them come up with cool new ways to do AI. 6. Changing AI for Good: DeepSeek is showing that AI can be cheaper and more open to everyone. They're changing how we think AI should be made and shared, which could shake up the whole AI world. So, as we watch DeepSeek, it's clear they're not just another player; they're changing the rules of the game. I predicted that this would be a make or break year for all the massive investments made in AI by American VC's. A few weeks later, DeepSeek happens! Watch the rest of my predictions in my 2025 outlook video . Link in replies #AIInnovation #DeepSeek #NVIDIA #OpenAI #TechDisruption

Dr Ola Brown

83,460 Aufrufe • vor 1 Jahr

𝐈 𝐟𝐨𝐮𝐧𝐝 𝐭𝐡𝐞 𝐀𝐯𝐚𝐭𝐚𝐫 𝐨𝐟 𝐀𝐈 𝐱 𝐁𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧 ➮ 𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐩𝐨𝐰𝐞𝐫, 𝐛𝐚𝐥𝐚𝐧𝐜𝐞, 𝐚𝐧𝐝 𝐬𝐩𝐞𝐞𝐝 𝐨𝐧 𝐒𝐨𝐥𝐚𝐧𝐚 ⏬ ✧ TARS AI > If you're sleeping on TARS AI 🤖, you’re behind the curve, but you’re still early enough to catch up. Don't walk.. run🍷 ✧ What is TARS AI about? Tars just launched the World’s First AI Token Marketplace - Where AI agents aren’t just services…They’re assets 𝙊𝙬𝙣𝙖𝙗𝙡𝙚 ~ 𝙏𝙧𝙖𝙙𝙖𝙗𝙡𝙚 ~𝙈𝙤𝙣𝙚𝙩𝙞𝙯𝙖𝙗𝙡𝙚..In under 5 minutes These agents are powered by OpenAI, Claude, DeepSeek, and more They learn from your on-chain and off-chain data and scale with bonding curves. This is type of AI you can clone from your X profile Imagine spinning up a digital version of you then staking it and trading it? Crazy right 𝐓𝐚𝐫𝐬 𝐣𝐮𝐬𝐭 𝐭𝐮𝐫𝐧𝐞𝐝 𝐢𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐢𝐧𝐭𝐨 𝐮𝐭𝐢𝐥𝐢𝐭𝐲 ✧ The real kicker? Tars is backed by Solana Foundation, NVIDIA, and top Web3 minds. It’s the AI layer Solana has been waiting for ➮ Agents = tokens ➮ Tokens = liquidity ➮ Liquidity = power •••• ••• ••••• ✧ Elon musk once said “𝘵𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦 𝘴𝘩𝘰𝘶𝘭𝘥 𝘭𝘰𝘰𝘬 𝘭𝘪𝘬𝘦 𝘵𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦” well it’s happening 𝐍𝐨𝐰 The testnet is live. The agents are active. The marketplace is moving. …and it’s only going to get louder.Everything AI 🤝 On-chain. AI is the new frontier and Tars is your gateway > Don't just use AI; Be the Avatar of it ✧ Explore now : [ X: TARS AI 🤖
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𝐈 𝐟𝐨𝐮𝐧𝐝 𝐭𝐡𝐞 𝐀𝐯𝐚𝐭𝐚𝐫 𝐨𝐟 𝐀𝐈 𝐱 𝐁𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧 ➮ 𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐩𝐨𝐰𝐞𝐫, 𝐛𝐚𝐥𝐚𝐧𝐜𝐞, 𝐚𝐧𝐝 𝐬𝐩𝐞𝐞𝐝 𝐨𝐧 𝐒𝐨𝐥𝐚𝐧𝐚 ⏬ ✧ TARS AI > If you're sleeping on TARS AI 🤖, you’re behind the curve, but you’re still early enough to catch up. Don't walk.. run🍷 ✧ What is TARS AI about? Tars just launched the World’s First AI Token Marketplace - Where AI agents aren’t just services…They’re assets 𝙊𝙬𝙣𝙖𝙗𝙡𝙚 ~ 𝙏𝙧𝙖𝙙𝙖𝙗𝙡𝙚 ~𝙈𝙤𝙣𝙚𝙩𝙞𝙯𝙖𝙗𝙡𝙚..In under 5 minutes These agents are powered by OpenAI, Claude, DeepSeek, and more They learn from your on-chain and off-chain data and scale with bonding curves. This is type of AI you can clone from your X profile Imagine spinning up a digital version of you then staking it and trading it? Crazy right 𝐓𝐚𝐫𝐬 𝐣𝐮𝐬𝐭 𝐭𝐮𝐫𝐧𝐞𝐝 𝐢𝐝𝐞𝐧𝐭𝐢𝐭𝐲 𝐢𝐧𝐭𝐨 𝐮𝐭𝐢𝐥𝐢𝐭𝐲 ✧ The real kicker? Tars is backed by Solana Foundation, NVIDIA, and top Web3 minds. It’s the AI layer Solana has been waiting for ➮ Agents = tokens ➮ Tokens = liquidity ➮ Liquidity = power •••• ••• ••••• ✧ Elon musk once said “𝘵𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦 𝘴𝘩𝘰𝘶𝘭𝘥 𝘭𝘰𝘰𝘬 𝘭𝘪𝘬𝘦 𝘵𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦” well it’s happening 𝐍𝐨𝐰 The testnet is live. The agents are active. The marketplace is moving. …and it’s only going to get louder.Everything AI 🤝 On-chain. AI is the new frontier and Tars is your gateway > Don't just use AI; Be the Avatar of it ✧ Explore now : [ X: TARS AI 🤖

O M A

34,227 Aufrufe • vor 1 Jahr

What if gaming paid you back not just in vibes, but in stuff you can own, trade, and flex? Ajuna Network crashing the scene with a blockchain-powered punch that’s about to change everything you know about gaming. Today we’re diving into Ajuna’s world of epic games that hit hard and a 2025 lineup so stacked it’s practically begging you to smash that play button! What is Ajuna Network? So, what’s Ajuna Network all about? It’s a decentralized gaming platform built on the Polkadot blockchain, giving developers the tools to craft games where you own your in-game assets as NFTs. And with their upcoming SAGE game engine, they’re about to supercharge the creative possibilities. This is gaming redefined, power to the players and creators, and it’s only getting bigger! Ajuna’s Games: The games that are stealing the show right now: AAA, Battle Mogs, and BBB. 1/AAA Awesome Ajuna Avatars NFT-powered collectible game that’s your ticket to their gaming universe. AAA lets you mint, forge, and evolve unique avatars tied to seasonal themes like Mogwai from Battle Mogs in Season 1 or Pets in Season 2. Each avatar’s an NFT you own, with over 5 million possible combos, and you level them up by sacrificing lower-tier ones to craft Legendary or even Mythical versions. The gameplay’s a mix of strategy and hustle. Your oices shape the adventure, and the ownership vibe makes every decision epic. Strategy and collecting fans, this one’s for you! 2/Battle Mog Tactical brilliance meets adorable Mogwai creatures. . The gameplay’s a mix of planning and adaptability. Outsmart your opponents in deep, strategic battles, and own your Mogwai as NFTs to trade or keep. For me, it’s the perfect blend of strategy and stakes, with NFT ownership making every victory feel massive. If you love a mental showdown, Battle Mogs is your jam! 3/BBB (Big Ballz of Bajun) Chaos, quirks, and pure fun! BBB’s a wild ride. A second collective-based game, a chaotic twist on the AAA formula that’s all about competition and creativity. Launched on the Bajun Network a free-to-play mobile battler where you mint, forge, and evolve quirky, street-art-inspired “Ballz” into Legendary status. These Ballz are NFTs you own, trade, or flex, with over 60 million possible variations thanks to five layers of customization. The goal? Climb leaderboards in tournaments like crafting the lowest Soul Point Legendary with a hype skill snagging a slice of juicy prize pool. But heads-up: it’s sunsetting soon! The devs are going out with a bang think special events and rewards. NFTs in Ajuna Network Games NFTs are the secret sauce here. In AAA and Battle Mogs, your avatars and Mogwai are unique, tradable assets. Grind for a rare Mogwai, trade it for something epic gaming and collecting collide, and it’s a rush. Better yet, these NFTs work across Ajuna’s games, so your victories carry weight everywhere. This is gaming with real stakes! Upcoming Games in 2025 Hold onto your controllers, because 2025 is about to go nuclear! Ajuna’s dropping a lineup that’ll make your gamer soul sing. A key milestone will be the highly anticipated completion of SAGE, their revolutionary game engine. By onboarding developers to this innovative platform, they’ll unlock fresh opportunities for engagement and creativity within the Ajuna ecosystem. Think more games, wilder ideas, and a flood of new ways to play , SAGE is the spark that’s lighting up Ajuna’s future! But wait, there’s more! Ajuna’s in-house crew is cooking up an action-packed roster: Hero Jam, Season 1 Avatars, Season 2 Pets, and an upgraded Battle Mogs. And looking ahead, Dot 4 Gravity and New Omega Reforged are on deck. Every title’s packing NFTs and AJUN token perks, making your gameplay more rewarding than ever. With so much on the horizon, the coming year is set to be nothing short of extraordinary! 2025 it’s a gaming revolution!
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What if gaming paid you back not just in vibes, but in stuff you can own, trade, and flex? Ajuna Network crashing the scene with a blockchain-powered punch that’s about to change everything you know about gaming. Today we’re diving into Ajuna’s world of epic games that hit hard and a 2025 lineup so stacked it’s practically begging you to smash that play button! What is Ajuna Network? So, what’s Ajuna Network all about? It’s a decentralized gaming platform built on the Polkadot blockchain, giving developers the tools to craft games where you own your in-game assets as NFTs. And with their upcoming SAGE game engine, they’re about to supercharge the creative possibilities. This is gaming redefined, power to the players and creators, and it’s only getting bigger! Ajuna’s Games: The games that are stealing the show right now: AAA, Battle Mogs, and BBB. 1/AAA Awesome Ajuna Avatars NFT-powered collectible game that’s your ticket to their gaming universe. AAA lets you mint, forge, and evolve unique avatars tied to seasonal themes like Mogwai from Battle Mogs in Season 1 or Pets in Season 2. Each avatar’s an NFT you own, with over 5 million possible combos, and you level them up by sacrificing lower-tier ones to craft Legendary or even Mythical versions. The gameplay’s a mix of strategy and hustle. Your oices shape the adventure, and the ownership vibe makes every decision epic. Strategy and collecting fans, this one’s for you! 2/Battle Mog Tactical brilliance meets adorable Mogwai creatures. . The gameplay’s a mix of planning and adaptability. Outsmart your opponents in deep, strategic battles, and own your Mogwai as NFTs to trade or keep. For me, it’s the perfect blend of strategy and stakes, with NFT ownership making every victory feel massive. If you love a mental showdown, Battle Mogs is your jam! 3/BBB (Big Ballz of Bajun) Chaos, quirks, and pure fun! BBB’s a wild ride. A second collective-based game, a chaotic twist on the AAA formula that’s all about competition and creativity. Launched on the Bajun Network a free-to-play mobile battler where you mint, forge, and evolve quirky, street-art-inspired “Ballz” into Legendary status. These Ballz are NFTs you own, trade, or flex, with over 60 million possible variations thanks to five layers of customization. The goal? Climb leaderboards in tournaments like crafting the lowest Soul Point Legendary with a hype skill snagging a slice of juicy prize pool. But heads-up: it’s sunsetting soon! The devs are going out with a bang think special events and rewards. NFTs in Ajuna Network Games NFTs are the secret sauce here. In AAA and Battle Mogs, your avatars and Mogwai are unique, tradable assets. Grind for a rare Mogwai, trade it for something epic gaming and collecting collide, and it’s a rush. Better yet, these NFTs work across Ajuna’s games, so your victories carry weight everywhere. This is gaming with real stakes! Upcoming Games in 2025 Hold onto your controllers, because 2025 is about to go nuclear! Ajuna’s dropping a lineup that’ll make your gamer soul sing. A key milestone will be the highly anticipated completion of SAGE, their revolutionary game engine. By onboarding developers to this innovative platform, they’ll unlock fresh opportunities for engagement and creativity within the Ajuna ecosystem. Think more games, wilder ideas, and a flood of new ways to play , SAGE is the spark that’s lighting up Ajuna’s future! But wait, there’s more! Ajuna’s in-house crew is cooking up an action-packed roster: Hero Jam, Season 1 Avatars, Season 2 Pets, and an upgraded Battle Mogs. And looking ahead, Dot 4 Gravity and New Omega Reforged are on deck. Every title’s packing NFTs and AJUN token perks, making your gameplay more rewarding than ever. With so much on the horizon, the coming year is set to be nothing short of extraordinary! 2025 it’s a gaming revolution!

Shelley

11,689 Aufrufe • vor 1 Jahr

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.
1:13

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,829 Aufrufe • vor 1 Jahr

💥 The Future Is Now: Pay Your Bills with Pi Using PrimePi Pay 💥 Powered by Pi. Built for the People. In a world racing toward decentralization and digital empowerment, one question still echoes for everyday people: When will crypto solve real problems? That time is now — and the answer is Pi Network. Introducing a revolutionary leap in the Pi ecosystem: a bold new app that finally lets you pay your real-world bills using Pi Coin (𝛑) — securely, instantly, and without relying on banks or middlemen. Welcome to PrimePi Pay — the bridge between blockchain freedom and the real-world responsibilities we all carry. 🔑 Why PrimePi Pay Matters Too many people are still stuck in a financial system that limits access, adds fees, and delays payments. Meanwhile, millions of Pioneers around the world have been quietly building a new financial layer — one mined on trust, time, and vision. Now it’s time to activate that vision. With PrimePi Pay, you’ll be able to: •Pay electricity, phone, internet, rent, and more using Pi •Scan bills and verify payment details with built-in AI tools •Send Pi directly to official businesses or trusted local agents •Track every payment inside your Pi wallet — fully transparent and secure ⚡ Real Utility. Real Adoption. Real Pi. This isn’t about hype. It’s about empowerment. You don’t need to convert to fiat. You don’t need to wait on banks. You don’t need permission. All you need is your Pi — and now, it can take care of your life’s most essential needs. PrimePi Pay is proudly powered by Pi — the people’s digital currency. 🧠 Powered by GenAI. Built by Pioneers. Using GenAI and Pi-native tools like Pi App Studio and Firebase, PrimePi Pay was created by Pioneers, for Pioneers. It’s simple. It’s powerful. And it’s laser-focused on solving real-world financial problems. It’s more than an app — it’s a global movement. You can even participate as a Prime Agent, helping users in your community pay bills while building a reputation inside the Pi economy. 🚀 PrimePi Pay: Just the Beginning As Pi Network continues its Open Mainnet expansion, PrimePi Pay will unlock: •Partnerships with major billers and utility companies •Mobile top-ups and rent payments in emerging markets •Local-to-global remittances, powered by trust and decentralization And guess what? It all starts with you. Your Pi. Your bills. Your power. 💬 Final Word: “One day, you’ll stop asking what Pi is worth. Instead, you’ll ask what you can do with it.” – A Pioneer of the New Economy Let’s make history. Let’s pay bills with PrimePi Pay. Powered by Pi. Designed for a new world. 💜🔌📲 #PrimePiPay #PoweredByPi #PiNetwork #PayWithPi #DecentralizeLife Pi Network Nicolas Kokkalis Chengdiao Fan
1:59

💥 The Future Is Now: Pay Your Bills with Pi Using PrimePi Pay 💥 Powered by Pi. Built for the People. In a world racing toward decentralization and digital empowerment, one question still echoes for everyday people: When will crypto solve real problems? That time is now — and the answer is Pi Network. Introducing a revolutionary leap in the Pi ecosystem: a bold new app that finally lets you pay your real-world bills using Pi Coin (𝛑) — securely, instantly, and without relying on banks or middlemen. Welcome to PrimePi Pay — the bridge between blockchain freedom and the real-world responsibilities we all carry. 🔑 Why PrimePi Pay Matters Too many people are still stuck in a financial system that limits access, adds fees, and delays payments. Meanwhile, millions of Pioneers around the world have been quietly building a new financial layer — one mined on trust, time, and vision. Now it’s time to activate that vision. With PrimePi Pay, you’ll be able to: •Pay electricity, phone, internet, rent, and more using Pi •Scan bills and verify payment details with built-in AI tools •Send Pi directly to official businesses or trusted local agents •Track every payment inside your Pi wallet — fully transparent and secure ⚡ Real Utility. Real Adoption. Real Pi. This isn’t about hype. It’s about empowerment. You don’t need to convert to fiat. You don’t need to wait on banks. You don’t need permission. All you need is your Pi — and now, it can take care of your life’s most essential needs. PrimePi Pay is proudly powered by Pi — the people’s digital currency. 🧠 Powered by GenAI. Built by Pioneers. Using GenAI and Pi-native tools like Pi App Studio and Firebase, PrimePi Pay was created by Pioneers, for Pioneers. It’s simple. It’s powerful. And it’s laser-focused on solving real-world financial problems. It’s more than an app — it’s a global movement. You can even participate as a Prime Agent, helping users in your community pay bills while building a reputation inside the Pi economy. 🚀 PrimePi Pay: Just the Beginning As Pi Network continues its Open Mainnet expansion, PrimePi Pay will unlock: •Partnerships with major billers and utility companies •Mobile top-ups and rent payments in emerging markets •Local-to-global remittances, powered by trust and decentralization And guess what? It all starts with you. Your Pi. Your bills. Your power. 💬 Final Word: “One day, you’ll stop asking what Pi is worth. Instead, you’ll ask what you can do with it.” – A Pioneer of the New Economy Let’s make history. Let’s pay bills with PrimePi Pay. Powered by Pi. Designed for a new world. 💜🔌📲 #PrimePiPay #PoweredByPi #PiNetwork #PayWithPi #DecentralizeLife Pi Network Nicolas Kokkalis Chengdiao Fan

Mr Spock 𝛑

15,731 Aufrufe • vor 1 Jahr

It isn't everyday I get to interview an Emmy winner (for his work building an interactive VR entertainment experience called "Wolves in the Walls" that blew my mind. Was the first to hand me a virtual item in such and talk with me inside a Holodeck. So awesome to catch up with Edward Saatchi and talk about his new company @fablesimulation and where the Holodeck is going next. ChatGPT wrote these notes about what you will learn by watching my interview: ++++++++++++ 🎬 What You’ll Learn from Edward Saatchi, CEO of Fable and Creator of Showrunner: The Future of Storytelling How AI is transforming movies into playable, user-driven worlds—think of watching a show, then remixing it into your own scenes the next day. The Holodeck is Real (Almost) What NVIDIA, Meta, and creators like Saatchi are doing to make the sci-fi Holodeck a near-future reality—blending LLMs, VR, and generative video. From Cinema to Simulation Why the next art form isn’t passive film or gaming—but something in between: AI-powered simulations populated by intelligent characters. The Role of the Artist Has Changed Directors become world-builders. Think: “Build Springfield, and let Springfield generate The Simpsons.” AI’s Cultural Moment Is Not About Cheap VFX Saatchi argues we shouldn’t waste AI on cost-cutting Pixar knockoffs—but instead embrace it as the foundation for new art movements. Playable IP Is the Future Imagine watching a Star Wars movie Friday, and by Sunday, fans have made thousands of new episodes using the same AI model. That’s the dream. A New Type of Social Network? In the age of AI-generated shows, what happens when every high school has its own AI drama? You might get a million versions of “Akron High.” Virtual Beings with Real Context Why convincing characters need more than chat—they need lives, families, coworkers, and a world to live in. Art as Reflection of Chaos Like the modernists after WWI, Saatchi sees AI as the medium that can reflect our fragmented, insane 2025 reality. The Role of the Human Creator Humans may shift from being the writer to being the simulator—designing environments, rules, and emotional logic that AI then plays out.
51:50

It isn't everyday I get to interview an Emmy winner (for his work building an interactive VR entertainment experience called "Wolves in the Walls" that blew my mind. Was the first to hand me a virtual item in such and talk with me inside a Holodeck. So awesome to catch up with Edward Saatchi and talk about his new company @fablesimulation and where the Holodeck is going next. ChatGPT wrote these notes about what you will learn by watching my interview: ++++++++++++ 🎬 What You’ll Learn from Edward Saatchi, CEO of Fable and Creator of Showrunner: The Future of Storytelling How AI is transforming movies into playable, user-driven worlds—think of watching a show, then remixing it into your own scenes the next day. The Holodeck is Real (Almost) What NVIDIA, Meta, and creators like Saatchi are doing to make the sci-fi Holodeck a near-future reality—blending LLMs, VR, and generative video. From Cinema to Simulation Why the next art form isn’t passive film or gaming—but something in between: AI-powered simulations populated by intelligent characters. The Role of the Artist Has Changed Directors become world-builders. Think: “Build Springfield, and let Springfield generate The Simpsons.” AI’s Cultural Moment Is Not About Cheap VFX Saatchi argues we shouldn’t waste AI on cost-cutting Pixar knockoffs—but instead embrace it as the foundation for new art movements. Playable IP Is the Future Imagine watching a Star Wars movie Friday, and by Sunday, fans have made thousands of new episodes using the same AI model. That’s the dream. A New Type of Social Network? In the age of AI-generated shows, what happens when every high school has its own AI drama? You might get a million versions of “Akron High.” Virtual Beings with Real Context Why convincing characters need more than chat—they need lives, families, coworkers, and a world to live in. Art as Reflection of Chaos Like the modernists after WWI, Saatchi sees AI as the medium that can reflect our fragmented, insane 2025 reality. The Role of the Human Creator Humans may shift from being the writer to being the simulator—designing environments, rules, and emotional logic that AI then plays out.

Robert Scoble

89,846 Aufrufe • vor 1 Jahr

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.
1:06

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.

Owen Gregorian

179,005 Aufrufe • vor 10 Monaten

$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!
7:18

$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

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