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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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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. 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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. 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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 Aufrufe • vor 5 Monaten

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 Aufrufe • vor 5 Monaten

🚨 #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 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 3 Jahren

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 Aufrufe • vor 5 Monaten