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NVIDIA Blackwell swept the new SemiAnalysis InferenceMAX v1 benchmarks, delivering the highest performance and best overall efficiency. InferenceMax v1 is the first independent benchmark to measure total cost of compute across diverse models and real-world scenarios. Best return on investment: NVIDIA GB200 NVL72 delivers unmatched AI factory economics — a $5 million investment generates $75 million in DSR1 token revenue, a 15x return on investment. Lowest total cost of ownership: NVIDIA B200 software optimizations achieve two cents per million tokens on gpt-oss, delivering 5x lower cost per token in just 2 months. Best throughput and interactivity: NVIDIA B200 sets the pace with 60,000 tokens per second per GPU and 1,000 tokens per second per user on gpt-oss with the latest NVIDIA TensorRT-LLM stack. As AI shifts from one-shot answers to complex reasoning, the demand for inference — and the economics behind it — is exploding. The new independent InferenceMAX v1 benchmarks are the first to measure total cost of compute across real-world scenarios. The results? The NVIDIA Blackwell platform swept the field — delivering unmatched performance and best overall efficiency for AI factories. A $5 million investment in an NVIDIA GB200 NVL72 system can generate $75 million in token revenue. That’s a 15x return on investment (ROI) — the new economics of inference. “Inference is where AI delivers value every day,” said Ian Buck, vice president of hyperscale and high-performance computing at NVIDIA. “These results show that NVIDIA’s full-stack approach gives customers the performance and efficiency they need to deploy AI at scale.” Enter InferenceMAX v1 InferenceMAX v1, a new benchmark from SemiAnalysis released Monday, is the latest to highlight Blackwell’s inference leadership. It runs popular models across leading platforms, measures performance for a wide range of use cases and publishes results anyone can verify. Why do benchmarks like this matter? Because modern AI isn’t just about raw speed — it’s about efficiency and economics at scale. As models shift from one-shot replies to multistep reasoning and tool use, they generate far more tokens per query, dramatically increasing compute demands. NVIDIA’s open-source collaborations with OpenAI (gpt-oss 120B), Meta (Llama 3 70B), and DeepSeek AI (DeepSeek R1) highlight how community-driven models are advancing state-of-the-art reasoning and efficiency. Partnering with these leading model builders and the open-source community, NVIDIA ensures the latest models are optimized for the world’s largest AI inference infrastructure. These efforts reflect a broader commitment to open ecosystems — where shared innovation accelerates progress for everyone. Deep collaborations with the FlashInfer, SGLang and vLLM communities enable codeveloped kernel and runtime enhancements that power these models at scale. Software Optimizations Deliver Continued Performance Gains NVIDIA continuously improves performance through hardware and software codesign optimizations. Initial gpt-oss-120b performance on an NVIDIA DGX Blackwell B200 system with the NVIDIA TensorRT LLM library was market-leading, but NVIDIA’s teams and the community have significantly optimized TensorRT LLM for open-source large language models. The TensorRT LLM v1.0 release is a major breakthrough in making large AI models faster and more responsive for everyone. Through advanced parallelization techniques, it uses the B200 system and NVIDIA NVLink Switch’s 1,800 GB/s bidirectional bandwidth to dramatically improve the performance of the gpt-oss-120b model. The innovation doesn’t stop there. The newly released gpt-oss-120b-Eagle3-v2 model introduces speculative decoding, a clever method that predicts multiple tokens at a time. This reduces lag and delivers even quicker results, tripling throughput at 100 tokens per second per user (TPS/user) — boosting per-GPU speeds from 6,000 to 30,000 tokens. For dense AI models like Llama 3.3 70B, which demand significant computational resources due to their large parameter count and the fact that all parameters are utilized simultaneously during inference, NVIDIA Blackwell B200 sets a new performance standard in InferenceMAX v1 benchmarks. Blackwell delivers over 10,000 TPS per GPU at 50 TPS per user interactivity — 4x higher per-GPU throughput compared with the NVIDIA H200 GPU. Performance Efficiency Drives Value Metrics like tokens per watt, cost per million tokens and TPS/user matter as much as throughput. In fact, for power-limited AI factories, Blackwell delivers 10x throughput per megawatt compared with the previous generation, which translates into higher token revenue. The cost per token is crucial for evaluating AI model efficiency, directly impacting operational expenses. The NVIDIA Blackwell architecture lowered cost per million tokens by 15x versus the previous generation, leading to substantial savings and fostering wider AI deployment and innovation. Multidimensional Performance InferenceMAX uses the Pareto frontier — a curve that shows the best trade-offs between different factors, such as data center throughput and responsiveness — to map performance. But it’s more than a chart. It reflects how NVIDIA Blackwell balances the full spectrum of production priorities: cost, energy efficiency, throughput and responsiveness. That balance enables the highest ROI across real-world workloads. Systems that optimize for just one mode or scenario may show peak performance in isolation, but the economics of that doesn’t scale. Blackwell’s full-stack design delivers efficiency and value where it matters most: in production. For a deeper look at how these curves are built — and why they matter for total cost of ownership and service-level agreement planning — check out this technical deep dive for full charts and methodology. What Makes It Possible? Blackwell’s leadership comes from extreme hardware-software codesign. It’s a full-stack architecture built for speed, efficiency and scale: The Blackwell architecture features include: NVFP4 low-precision format for efficiency without loss of accuracy Fifth-generation NVIDIA NVLink that connects 72 Blackwell GPUs to act as one giant GPU NVLink Switch, which enables high concurrency through advanced tensor, expert and data parallel attention algorithms Annual hardware cadence plus continuous software optimization — NVIDIA has more than doubled Blackwell performance since launch using software alone NVIDIA TensorRT-LLM, NVIDIA Dynamo, SGLang and vLLM open-source inference frameworks optimized for peak performance A massive ecosystem, with hundreds of millions of GPUs installed, 7 million CUDA developers and contributions to over 1,000 open-source projects The Bigger Picture AI is moving from pilots to AI factories — infrastructure that manufactures intelligence by turning data into tokens and decisions in real time. Open, frequently updated benchmarks help teams make informed platform choices, tune for cost per token, latency service-level agreements and utilization across changing workloads. NVIDIA’s Think SMART framework helps enterprises navigate this shift, spotlighting how NVIDIA’s full-stack inference platform delivers real-world ROI — turning performance into profits. View the full article

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Microsoft Azure today announced the new NDv6 GB300 VM series, delivering the industry’s first supercomputing-scale production cluster of NVIDIA GB300 NVL72 systems, purpose-built for OpenAI’s most demanding AI inference workloads. This supercomputer-scale cluster features over 4,600 NVIDIA Blackwell Ultra GPUs connected via the NVIDIA Quantum-X800 InfiniBand networking platform. Microsoft’s unique systems approach applied radical engineering to memory and networking to provide the massive scale of compute required to achieve high inference and training throughput for reasoning models and agentic AI systems. Today’s achievement is the result of years of a deep partnership between NVIDIA and Microsoft purpose-building AI infrastructure for the world’s most demanding AI workloads and to deliver infrastructure for the next frontier of AI. It marks another leadership moment, ensuring that leading-edge AI drives innovation in the United States. “Delivering the industry’s first at-scale NVIDIA GB300 NVL72 production cluster for frontier AI is an achievement that goes beyond powerful silicon — it reflects Microsoft Azure and NVIDIA’s shared commitment to optimize all parts of the modern AI data center,” said Nidhi Chappell, corporate vice president of Microsoft Azure AI Infrastructure. “Our collaboration helps ensure customers like OpenAI can deploy next-generation infrastructure at unprecedented scale and speed.” Inside the Engine: The NVIDIA GB300 NVL72 At the heart of Azure’s new NDv6 GB300 VM series is the liquid-cooled, rack-scale NVIDIA GB300 NVL72 system. Each rack is a powerhouse, integrating 72 NVIDIA Blackwell Ultra GPUs and 36 NVIDIA Grace CPUs into a single, cohesive unit to accelerate training and inference for massive AI models. The system provides a staggering 37 terabytes of fast memory and 1.44 exaflops of FP4 Tensor Core performance per VM, creating a massive, unified memory space essential for reasoning models, agentic AI systems and complex multimodal generative AI. NVIDIA Blackwell Ultra is supported by the full-stack NVIDIA AI platform, including collective communication libraries that tap into new formats like NVFP4 for breakthrough training performance, as well as compiler technologies like NVIDIA Dynamo for the highest inference performance in reasoning AI. The NVIDIA Blackwell Ultra platform excels at both training and inference. In the recent MLPerf Inference v5.1 benchmarks, NVIDIA GB300 NVL72 systems delivered record-setting performance using NVFP4. Results included up to 5x higher throughput per GPU on the 671-billion-parameter DeepSeek-R1 reasoning model compared with the NVIDIA Hopper architecture, along with leadership performance on all newly introduced benchmarks like the Llama 3.1 405B model. The Fabric of a Supercomputer: NVLink Switch and NVIDIA Quantum-X800 InfiniBand To connect over 4,600 Blackwell Ultra GPUs into a single, cohesive supercomputer, Microsoft Azure’s cluster relies on a two-tiered NVIDIA networking architecture designed for both scale-up performance within the rack and scale-out performance across the entire cluster. Within each GB300 NVL72 rack, the fifth-generation NVIDIA NVLink Switch fabric provides 130 TB/s of direct, all-to-all bandwidth between the 72 Blackwell Ultra GPUs. This transforms the entire rack into a single, unified accelerator with a shared memory pool — a critical design for massive, memory-intensive models. To scale beyond the rack, the cluster uses the NVIDIA Quantum-X800 InfiniBand platform, purpose-built for trillion-parameter-scale AI. Featuring NVIDIA ConnectX-8 SuperNICs and Quantum-X800 switches, NVIDIA Quantum-X800 provides 800 Gb/s of bandwidth per GPU, ensuring seamless communication across all 4,608 GPUs. Microsoft Azure’s cluster also uses NVIDIA Quantum-X800’s advanced adaptive routing, telemetry-based congestion control and performance isolation capabilities, as well as NVIDIA Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) v4, which accelerates operations to significantly boost the efficiency of large-scale training and inference. Driving the Future of AI Delivering the world’s first production NVIDIA GB300 NVL72 cluster at this scale required a reimagination of every layer of Microsoft’s data center — from custom liquid cooling and power distribution to a reengineered software stack for orchestration and storage. This latest milestone marks a big step forward in building the infrastructure that will unlock the future of AI. As Azure scales to its goal of deploying hundreds of thousands of NVIDIA Blackwell Ultra GPUs, even more innovations are poised to emerge from customers like OpenAI. Learn more about this announcement on the Microsoft Azure blog. View the full article

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