# NVIDIA Vera Rubin NVL72

NVIDIA reframes AI-factory memory from how much capacity a GPU has to how many useful tokens each byte produces, combining NVFP4 weight and KV-cache compression, wide expert parallelism for MoE weights, Dynamo KV-cache reuse, Vera CPU staging of context during tool calls, and CMX pod-level context storage. On SemiAnalysis AgentX, which replays recorded coding-agent sessions, Vera Rubin NVL72 delivers up to 30x the throughput per megawatt of GB300 NVL72 at 160 tokens per second per user, with both at 288 GB per GPU.

NVIDIA reframes AI-factory memory from how much capacity a GPU has to how many useful tokens each byte produces, combining NVFP4 weight and KV-cache compression, wide expert parallelism for MoE weights, Dynamo KV-cache reuse, Vera CPU staging of context during tool calls, and CMX pod-level context storage. On SemiAnalysis AgentX, which replays recorded coding-agent sessions, Vera Rubin NVL72 delivers up to 30x the throughput per megawatt of GB300 NVL72 at 160 tokens per second per user, with both at 288 GB per GPU.

NVIDIA describes the deterministic execution model of the Groq 3 LPX low-latency accelerator within its Vera Rubin platform: the LPU compiler schedules compute and data movement down to the clock cycle, letting it predict per-cycle current demand and use Preemptive Power (PEP) and Clock Period Synthesis (CPS) to reduce voltage droop and shrink the voltage guardband, with internal testing showing over 60% less voltage drop, an estimated high single-digit percentage decrease in the baseline voltage that must be continually supplied, a potentially low-double-digit percentage reduction in power for the same workload, and up to 35x higher throughput per megawatt at the platform level versus the previous-generation GB200 NVL72 for 2T+ parameter models at long context and high interactivity.

NVIDIA describes the deterministic execution model of the Groq 3 LPX low-latency accelerator within its Vera Rubin platform: the LPU compiler schedules compute and data movement down to the clock cycle, letting it predict per-cycle current demand and use Preemptive Power (PEP) and Clock Period Synthesis (CPS) to reduce voltage droop and shrink the voltage guardband, with internal testing showing over 60% less voltage drop, an estimated high single-digit percentage decrease in the baseline voltage that must be continually supplied, a potentially low-double-digit percentage reduction in power for the same workload, and up to 35x higher throughput per megawatt at the platform level versus the previous-generation GB200 NVL72 for 2T+ parameter models at long context and high interactivity.

At Fully Connected, CoreWeave announced availability of NVIDIA Vera Rubin NVL72 systems with Spectrum-X 102.4T Ethernet on CoreWeave Cloud, with first customer Cognition benchmarking a real-world software engineering workload built from a sampled subset of FrontierCode tasks and seeing up to a 4.8x increase in total token throughput for SWE-2 inference over GB200 NVL72; CoreWeave also said it will offer NVIDIA Vera, described as the first CPU built for AI agents, and launched CoreWeave Forge, a connected environment unifying Weights & Biases, OpenPipe post-training expertise and the open source marimo notebook project.

At Fully Connected, CoreWeave announced availability of NVIDIA Vera Rubin NVL72 systems with Spectrum-X 102.4T Ethernet on CoreWeave Cloud, with first customer Cognition benchmarking a real-world software engineering workload built from a sampled subset of FrontierCode tasks and seeing up to a 4.8x increase in total token throughput for SWE-2 inference over GB200 NVL72; CoreWeave also said it will offer NVIDIA Vera, described as the first CPU built for AI agents, and launched CoreWeave Forge, a connected environment unifying Weights & Biases, OpenPipe post-training expertise and the open source marimo notebook project.

NVIDIA's first MLPerf Inference v6.1 preview submission of Vera Rubin NVL72 reports up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and up to 2.5x on DeepSeek-R1, while GB300 NVL72 reached 99% scaling efficiency across 288 GPUs in four racks and software optimizations delivered up to 1.6x over v6.0.

NVIDIA's first MLPerf Inference v6.1 preview submission of Vera Rubin NVL72 reports up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and up to 2.5x on DeepSeek-R1, while GB300 NVL72 reached 99% scaling efficiency across 288 GPUs in four racks and software optimizations delivered up to 1.6x over v6.0.