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Vera Rubin NVL72 MLPerf v6.1 Debut: Qwen3-VL Throughput 3.7x GB300, CoreWeave Brings Multi-Rack Cluster Online Same Day

· 5 min read
AI Compute Cards Wiki Editorial
Industry Research Team

On September 16, MLCommons released the MLPerf Inference v6.1 Closed Division results, and NVIDIA's Vera Rubin NVL72 completed its benchmark debut. The same day, CoreWeave announced that a multi-rack Vera Rubin cluster was live on its cloud — a "rent the new hardware on launch day" cadence that is compressing the cycle from next-generation compute announcement to billable output down to a quarter.

1. Debut Results: 3.7x and 2.5x​

NVIDIA, via preview submissions (entries 6.1-0106 / 6.1-0074), provided an apples-to-apples comparison of its two flagship generations:

Benchmark ModelVera Rubin NVL72 vs GB300 NVL72Software Stack
Qwen3-VL-235B-A22B (235B multimodal MoE)Throughput up to 3.7x (across offline / server / interactive scenarios)vLLM + NVIDIA Dynamo
DeepSeek-R1-671B (671B inference model)Throughput up to 2.5xTensorRT-LLM

The three technologies behind these numbers:

  • NVFP4 precision: compresses the memory footprint of model weights, attention tensors, and the KV Cache, enabling significantly larger effective batch sizes;
  • Disaggregated serving: prefill (compute-intensive) and decode (bandwidth-intensive) are split across separate GPU pools, each independently optimized;
  • Expert parallelism: MoE expert layers route across chips in a distributed fashion, paired with sixth-generation NVLink / NVLink Switch (NVIDIA claims 10x the packet rate of commodity Ethernet at 3x lower latency).

2. Same-Session Highlights: Scaling Efficiency and the Software Dividend​

  • GB300 four racks, 288 chips, 99% scaling efficiency: in the DeepSeek-R1 offline scenario, scaling from a single rack of 72 chips to 4 racks of 288 chips shows almost no loss (entries 6.1-0073 / 6.1-0074) — the first time rack-scale interconnect scaling efficiency has been validated at the 288-chip level;
  • The software dividend is still being paid out: on the same GB300 hardware, v6.1 software optimizations deliver up to 1.6x Qwen3-VL gains over v6.0; NVIDIA also reported post-submission gains for GPT-OSS-120B and DLRMv3 (not verified by MLCommons);
  • 19 partners submitted, 8 of them using multi-node Blackwell NVL72 configurations (ASUS, Azure, Cisco, CoreWeave, Crusoe, Dell, Fujitsu, HPE, Lambda, Nebius, OCI, Supermicro, and others); Nebius also submitted Vera Rubin preview results.

3. CoreWeave Online the Same Day: Rentable at Launch​

On the same day the MLPerf results were published, CoreWeave announced that its multi-rack Vera Rubin NVL72 cluster was live on its cloud:

  • Spectrum-X networking links hundreds of Rubin GPUs into a single scale-out cluster, with each GPU paired with 2 ConnectX-9 SuperNICs (1.6Tb/s scale-out bandwidth);
  • The AI object store LOTA cuts read latency by 8x;
  • On the operations side: Valvey software-defined liquid cooling (NVIDIA spec: 45°C liquid inlet), the Racky rack control layer, and Rack LifeCycle Controller manage an entire NVL72 rack as a single programmable entity;
  • Back in July, CoreWeave's own testing put Vera Rubin at 10x tokens/s/MW versus GB200 NVL72 on DeepSeek R1 (company figures, not MLCommons-verified).

4. The Competitive Picture​

  • AMD + Crusoe: set MLPerf's all-time high aggregate throughput of 5.75M tokens/s on 512 chips (AMD had the broadest submission coverage this round);
  • SemiAnalysis AgentX preview: Vera Rubin up to 30x over GB300 on agentic workloads (preview figures); the MLPerf Endpoints benchmark is forthcoming and will bring agentic inference into standardized measurement;
  • Pending validation: Vera Rubin results are preview submissions awaiting independent reproduction; no quantified quality metrics were given for NVFP4's "virtually lossless" claim; per-token cost and power-consumption scaling comparisons have not been published. Matching submissions from Google TPU v7 and AMD MI455X UALoE72 are expected within 2026.

On-site spec comparisons: Rubin R200 (288GB / 22TB/s), B300 Ultra (GB300-class core).

5. Takeaways​

  • Vera Rubin NVL72 debut: Qwen3-VL 3.7x, DeepSeek-R1 2.5x over GB300 NVL72 (preview figures);
  • GB300 across four racks of 288 chips at 99% scaling efficiency — rack-scale interconnects have entered the usable range;
  • CoreWeave brought a multi-rack cluster online the same day — the cycle from next-gen compute launch to billable output is now measured in quarters;
  • What to watch: independent reproductions and formal (non-preview) submissions, TPU v7 / MI455X comparison results, and measured per-MW token throughput.

Further Reading​

References​

  • NVIDIA Blog: Vera Rubin NVL72 Makes Its First Appearance in MLPerf Inference v6.1 (2026-09-16)
  • MLCommons: MLPerf Inference v6.1 Closed Division results (entries 6.1-0073 / 6.1-0074 / 6.1-0106)
  • CoreWeave Investor Relations: multi-rack Vera Rubin NVL72 availability announcement (2026-09-16)
  • AMD Blogs: MLPerf Inference v6.1 submission results

This article is compiled from public MLCommons results and vendor official statements. Vera Rubin results are preview submissions; some gain figures are not MLCommons-verified and are flagged item by item.