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Lighten the lift of high performance workloads. ☁️💻💡 Amazon #EC2 Hpc7a instances deliver 300 Gbps with EFA & up to 192 cores of 4th gen AMD EPYC processors—to scale compute-intensive, latency-sensitive HPC workloads efficiently for faster results. 👉

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Amazon Web Services

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41,224 просмотров • 3 лет назад •via X (Twitter)

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Amazon Web Services

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AWS has announced the general availability of its new Amazon EC2 R8a memory-optimized instances, powered by 5th Gen AMD EPYC processors with clock speeds up to 4.5 GHz. This means more speed and efficiency at a lower total cost of ownership, without having to change their cloud architecture. Read more from Amazon Web Services:
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$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.
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$AMD $AMZN partnership will 🚀 in 2026 🔥 Amazon/AMD partnership is hidden among hot headlines from OpenAI $NVDA $ORCL... TLDR: Amazon refused to bid up the overpriced $NVDA chips among other hyperscalers, and decided to work closely with $AMD. Amazon is expected to spend up to $10-$20B a year on 2026 EPYC breakthrough Gen and Future Gen. Dr. Su confirmed "we have plenty for other large customers". For its 2026 EPYC "Venice" processors, AMD is using a multi-node manufacturing strategy: the CPU core complex dies (CCDs) are built on TSMC's 2 nm-class node (N2), while the I/O die (IOD) uses the N3P (3 nm) process. Context: Andy Jassy Amazon Web Services has been working with AMD on EPYC processors since November 2018. With this "secret weapon" breakthrough(patented), this long time partnership has expanded to New breakthrough 2026 EPYC Gen. AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for AWS, enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. AMD to benefit AWS's $100B+ AI opportunity along with $ORCL $MSFT $GOOGL $META Saudi, UAE ,38+ countries and startups. In early October, Amazon/AWS announced the new EC2 M8a instances as their latest-generation, general-purpose compute instances now powered by AMD EPYC 9005 "Turin" processors. Amazon announced the M8a as having up to 30% higher performance and up to 19% better price performance over M7a. With my testing of both at 32 vCPUs, the new AMD EPYC Turin instance provided 1.59x the performance over the prior-generation EPYC Genoa instance! How will this impact AWS AI Inference? ~Cost Efficiency: Inference is 80%+ of AI workloads and latency-sensitive (e.g., chatbots need <1s responses). "Secret weapon" enables 35x better inference perf (per AMD's CDNA roadmap tie-in), cutting AWS's energy use by 50%+ in clusters. With $118B 2025 capex, this could save $20–$30B annually in OPEX, boosting margins to 35%-40%. ~Scalability for Agentic AI: Supports "Helios" rack-scale platforms (up to 128 GPUs + EPYC hosts), delivering 3.58x FP6 perf for distributed inference. AWS can run 700K+ more tokens/sec in 1,000-node clusters (via EPYC 9575F boosts), enabling real-time apps like personalized search or fraud detection at enterprise scale. ~Adoption Catalysts: Early partners like Oracle signal broad uptake; AWS's existing AMD instances G4ad with Radeon GPUs) pave the way. By 2026, EPYC could power 40%+ of AWS AI infra, outpacing Nvidia's GPU lock-in via open standards (ROCm 8 software). Lastly, Amazon’s trajectory toward a $320 stock price is not a speculative leap but a grounded projection rooted in its unmatched fundamentals and strategic AI leadership. With Amazon Web Services poised to surpass $100 billion in annual revenue by 2026, driven by explosive AI inference demand, Amazon is redefining cloud computing’s future. The adoption of AMD’s 2026 EPYC processors with "Secret" architecture is a game-changer, slashing costs by up to 50% and boosting inference throughput 3x, enabling AWS to dominate enterprise AI workloads with unmatched efficiency. This technological edge, combined with Amazon’s e-commerce dominance and high-margin advertising growth, supports a valuation rerating to 22x EV/EBITDA, and it is still a discount to historical highs. Trading at $222, $AMZN is undervalued for its 15–20% revenue CAGR and 25%+ EPS growth through 2030.

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El Capitan delivers a level of performance and efficiency unimaginable just a few years ago. AMD's Lisa Su joins #LLNL to discuss how the AMD MI300A processors powering the machine's extraordinary capabilities will optimize #AI and #HPC for modeling and simulation workloads.
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El Capitan delivers a level of performance and efficiency unimaginable just a few years ago. AMD's Lisa Su joins #LLNL to discuss how the AMD MI300A processors powering the machine's extraordinary capabilities will optimize #AI and #HPC for modeling and simulation workloads.

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OVHcloud

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

Mike

141,018 просмотров • 9 месяцев назад

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AI agents move fast. So should your search foundation. Introducing the next generation of Amazon OpenSearch Serverless. We rebuilt OpenSearch Serverless from the ground up for agentic AI and unpredictable workloads: - Create resources in seconds - Autoscale up to 20x faster than before - Usage-based pricing with decoupled storage and compute - No idle costs with scale to zero - Up to 60% cost savings vs. provisioned clusters for peak capacity Learn more.

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$AMD is becoming a two-engine AI story with EPYC CPUs for the growing compute needed around agentic workloads and Instinct GPUs for hyperscalers that need a credible alternative to $NVDA. The CPU side still looks underappreciated as AI agents require more high-core-count server compute and 2nm Venice should help AMD keep taking share as $INTC works through roadmap and manufacturing challenges. The bridge is Helios where AMD can package EPYC CPUs, Instinct GPUs and Pensando networking into a full rack-scale AI platform backed by real demand from $META, $ORCL and OpenAI.

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Over 2,000 NVIDIA B300 GPUs are going live in a mega-cluster on Aethir's decentralized cloud very soon. ☁️ This is next-level infrastructure that unlocks access for the most ambitious AI workloads and use cases. 💻 Enterprise-grade GPU compute, ready to scale with your ambitions. ⚡️ Listen to Mark Rydon, our Co-Founder, explain the importance of Aethir’s B300 deployment.

Aethir

12,781 просмотров • 5 месяцев назад

We're teaming up with Cerebras to build the fastest possible inference. Coming soon to Amazon Bedrock, we’re delivering inference performance an order of magnitude faster than what’s available today by connecting AWS Trainium3 for compute-intensive prefill with Cerebras CS-3 to power decode. Learn more about the partnership.
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We're teaming up with Cerebras to build the fastest possible inference. Coming soon to Amazon Bedrock, we’re delivering inference performance an order of magnitude faster than what’s available today by connecting AWS Trainium3 for compute-intensive prefill with Cerebras CS-3 to power decode. Learn more about the partnership.

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205,362 просмотров • 5 месяцев назад

Huawei Cloud aC8, the latest general computing-plus Elastic Cloud Server (ECS), redefines performance for compute-intensive workloads such as film rendering, gaming, and AI inference. With an impressive capacity of up to 384 vCPUs, aC8 maintains stable performance even under extreme workloads. Built with unmatched scalability and flexibility, aC8 supports dynamic scaling across various deployment forms. Compared to the previous generation, aC8 delivers 25% higher performance and over 50% better cost-effectiveness. aC8 is available in China, Singapore, Thailand, Brazil, and more. Learn more: #HuaweiCloud #ECS #ElasticCloudServer #aC8instances #Techtalk
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Huawei Cloud aC8, the latest general computing-plus Elastic Cloud Server (ECS), redefines performance for compute-intensive workloads such as film rendering, gaming, and AI inference. With an impressive capacity of up to 384 vCPUs, aC8 maintains stable performance even under extreme workloads. Built with unmatched scalability and flexibility, aC8 supports dynamic scaling across various deployment forms. Compared to the previous generation, aC8 delivers 25% higher performance and over 50% better cost-effectiveness. aC8 is available in China, Singapore, Thailand, Brazil, and more. Learn more: #HuaweiCloud #ECS #ElasticCloudServer #aC8instances #Techtalk

Huawei Cloud

21,239 просмотров • 11 месяцев назад

During his AWS re:Invent keynote today, Amazon Web Services CEO Matt Garman emphasized the importance of the company’s strategic partnership with HUMAIN. Together, we’re creating a first-of-its-kind AI Zone in Saudi Arabia, built to serve global enterprises, tech innovators, and the rapidly rising demand for compute. Leveraging abundant power resources, the AI Zone is designed to support high-demand AI workloads at global scale. #HUMAINAI #TheEndOfLimits
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During his AWS re:Invent keynote today, Amazon Web Services CEO Matt Garman emphasized the importance of the company’s strategic partnership with HUMAIN. Together, we’re creating a first-of-its-kind AI Zone in Saudi Arabia, built to serve global enterprises, tech innovators, and the rapidly rising demand for compute. Leveraging abundant power resources, the AI Zone is designed to support high-demand AI workloads at global scale. #HUMAINAI #TheEndOfLimits

HUMAIN

10,162 просмотров • 8 месяцев назад

Compute is the backbone of the AI-driven future. OptimAI Compute Engine would enable scalable, high-performance workloads across image and video models, supporting: • Text-to-image generation • Image editing and inpainting • 2K / 4K / 8K super-resolution • Brand-aligned visual synthesis • OCR and image intelligence • Video generation and motion synthesis • Frame interpolation and enhancement • Multimodal model inference Distributed compute built for faster inference, shorter training cycles, and production-grade AI execution.
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Compute is the backbone of the AI-driven future. OptimAI Compute Engine would enable scalable, high-performance workloads across image and video models, supporting: • Text-to-image generation • Image editing and inpainting • 2K / 4K / 8K super-resolution • Brand-aligned visual synthesis • OCR and image intelligence • Video generation and motion synthesis • Frame interpolation and enhancement • Multimodal model inference Distributed compute built for faster inference, shorter training cycles, and production-grade AI execution.

OptimAI Network

18,845 просмотров • 6 месяцев назад

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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.

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20,832 просмотров • 7 месяцев назад

The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.
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The biggest driver of AI compute now is real-world deployment. At NVIDIA GTC, IREN's Kent Draper, alongside Lenovo's Vlad Rozanovich, shared what they’re actually seeing on the ground from AI infrastructure demand, and the signal is coming from enterprise use. He pointed to agents, internal enterprise models, and real-world deployments as the workloads now driving demand, and explained that the challenge isn't finding customers, it’s how quickly they can build capacity to keep up. Their strategy? A vertically integrated model focused entirely on power-dense computing, which allows them to scale faster, control the environment, and deliver infrastructure purpose-built for AI rather than multi-tenant data centers trying to support dozens of different workloads.

Six Five Media

7,422,478 просмотров • 4 месяцев назад

Deep inside a Norwegian mountain lies one of the most unusual HPC environments in the world: Lefdal Mine DC, where cold seawater siphoned from a nearby fjord cools dense GPU clusters designed for large-scale AI workloads. HRT’s deployment at Lefdal includes DLC racks operating above 125 kW per cabinet, integrated with high-speed interconnects and bespoke storage systems designed to power our cutting-edge quantitative research. In our latest video, we take a look inside the mine at our next-gen HPC and networking infrastructure:
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Deep inside a Norwegian mountain lies one of the most unusual HPC environments in the world: Lefdal Mine DC, where cold seawater siphoned from a nearby fjord cools dense GPU clusters designed for large-scale AI workloads. HRT’s deployment at Lefdal includes DLC racks operating above 125 kW per cabinet, integrated with high-speed interconnects and bespoke storage systems designed to power our cutting-edge quantitative research. In our latest video, we take a look inside the mine at our next-gen HPC and networking infrastructure:

Hudson River Trading

160,515 просмотров • 5 месяцев назад

🚨 #1 on IO500 — DDN is the Undisputed Leader 🚨 The results are in — and the numbers speak for themselves. When your mission is curing disease, securing global markets, or building the future of AI, your infrastructure can't just keep up. It has to lead. 📊 On the latest IO500 benchmark, DDN: - Outperformed Weka by 3.3x - Outpaced VAST by 11x - Delivered real-world results that translate to more jobs per day, faster outcomes, and millions saved in compute costs. 💡 Whether you're training LLMs, running simulations, or powering next-gen analytics, performance matters — and DDN delivers. 🎯 Built for AI. Built for HPC. Built to Lead. 🔗 Explore the full IO500 results in our blog → #IO500 #AIInfrastructure #HPC #AI #DDN #DataIntelligence #PerformanceMatters #AILeadership #GPUReady #ExascaleReady
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🚨 #1 on IO500 — DDN is the Undisputed Leader 🚨 The results are in — and the numbers speak for themselves. When your mission is curing disease, securing global markets, or building the future of AI, your infrastructure can't just keep up. It has to lead. 📊 On the latest IO500 benchmark, DDN: - Outperformed Weka by 3.3x - Outpaced VAST by 11x - Delivered real-world results that translate to more jobs per day, faster outcomes, and millions saved in compute costs. 💡 Whether you're training LLMs, running simulations, or powering next-gen analytics, performance matters — and DDN delivers. 🎯 Built for AI. Built for HPC. Built to Lead. 🔗 Explore the full IO500 results in our blog → #IO500 #AIInfrastructure #HPC #AI #DDN #DataIntelligence #PerformanceMatters #AILeadership #GPUReady #ExascaleReady

DDN

35,069 просмотров • 1 год назад

Canonical and Intel collaborate to deliver real-time Ubuntu on 12th Gen Intel® Core™ processors with TSN and TCC Support. The collaboration enables enterprises to harness the power of optimised Linux on the latest silicon for a wide range of use cases, from telco workloads to life-saving medical equipment, and automation systems for the factory floor. ▶️ Discover more about our partnership and the benefits of real-time solutions by watching this video. Watch the full playlist here: #intel #realtime #rtos #silicon #industrial
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Canonical and Intel collaborate to deliver real-time Ubuntu on 12th Gen Intel® Core™ processors with TSN and TCC Support. The collaboration enables enterprises to harness the power of optimised Linux on the latest silicon for a wide range of use cases, from telco workloads to life-saving medical equipment, and automation systems for the factory floor. ▶️ Discover more about our partnership and the benefits of real-time solutions by watching this video. Watch the full playlist here: #intel #realtime #rtos #silicon #industrial

Canonical

19,867 просмотров • 3 лет назад

AI in the network is shifting the conversation beyond GPUs. At MWC 2026, Intel's Kevork Kechichian, EVP & GM of the Data Center Group, explains why inference workloads are bringing CPUs back into focus, especially in telco environments where power, space, and efficiency matter. With platforms like Intel Xeon and Clearwater Forest, operators are seeing up to 30% performance gains and 60% better power efficiency, helping scale AI workloads without overhauling infrastructure. As networks evolve toward AI-driven operations and 6G, one principle is becoming clear: The future of AI infrastructure is about using the right compute for the right workload.
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AI in the network is shifting the conversation beyond GPUs. At MWC 2026, Intel's Kevork Kechichian, EVP & GM of the Data Center Group, explains why inference workloads are bringing CPUs back into focus, especially in telco environments where power, space, and efficiency matter. With platforms like Intel Xeon and Clearwater Forest, operators are seeing up to 30% performance gains and 60% better power efficiency, helping scale AI workloads without overhauling infrastructure. As networks evolve toward AI-driven operations and 6G, one principle is becoming clear: The future of AI infrastructure is about using the right compute for the right workload.

Six Five Media

131,501 просмотров • 5 месяцев назад

Today, Core Scientific and CoreWeave announced a $1.2 billion expansion at Core Scientific's Denton, Texas site. -Core Scientific and CoreWeave announce 70-megawatt (MW) expansion at the Denton, TX site -Expands to ~ 590 MW of contracted HPC infrastructure across 6 sites -Boosts total projected revenue to $10.2 billion over 12-year contract terms “We are thrilled to deepen our relationship with CoreWeave as we continue developing large-scale HPC projects that power advanced AI and other low-latency workloads. By expanding our capacity in Denton, we’re building one of the largest GPU supercomputers in North America — reinforcing Core Scientific’s leadership in delivering high-density, high-performance digital infrastructure. We look forward to delivering even greater value for both our customers and shareholders.” $CORZ CEO Adam Sullivan Read the full press release:
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Today, Core Scientific and CoreWeave announced a $1.2 billion expansion at Core Scientific's Denton, Texas site. -Core Scientific and CoreWeave announce 70-megawatt (MW) expansion at the Denton, TX site -Expands to ~ 590 MW of contracted HPC infrastructure across 6 sites -Boosts total projected revenue to $10.2 billion over 12-year contract terms “We are thrilled to deepen our relationship with CoreWeave as we continue developing large-scale HPC projects that power advanced AI and other low-latency workloads. By expanding our capacity in Denton, we’re building one of the largest GPU supercomputers in North America — reinforcing Core Scientific’s leadership in delivering high-density, high-performance digital infrastructure. We look forward to delivering even greater value for both our customers and shareholders.” $CORZ CEO Adam Sullivan Read the full press release:

Core Scientific

21,180 просмотров • 1 год назад