{"id":972752,"date":"2026-06-16T11:03:51","date_gmt":"2026-06-16T15:03:51","guid":{"rendered":"https:\/\/www.marketnewsdesk.com\/index.php\/coreweave-sets-new-ai-training-records-in-mlperf-training-v6-0-training-deepseek-v3-in-approximately-two-minutes\/"},"modified":"2026-06-16T11:03:51","modified_gmt":"2026-06-16T15:03:51","slug":"coreweave-sets-new-ai-training-records-in-mlperf-training-v6-0-training-deepseek-v3-in-approximately-two-minutes","status":"publish","type":"post","link":"https:\/\/www.marketnewsdesk.com\/index.php\/coreweave-sets-new-ai-training-records-in-mlperf-training-v6-0-training-deepseek-v3-in-approximately-two-minutes\/","title":{"rendered":"CoreWeave Sets New AI Training Records in MLPerf\u00ae Training v6.0, Training DeepSeek-V3 in Approximately Two Minutes"},"content":{"rendered":"

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CoreWeave Sets New AI Training Records in MLPerf\u00ae Training v6.0, Training DeepSeek-V3 in Approximately Two Minutes<\/b><\/p>\n

Full-stack infrastructure optimizations across the largest NVIDIA GB300 NVL72 cluster in the benchmark deliver record-breaking training performance for the world’s most demanding frontier models<\/i><\/p>\n

LIVINGSTON, N.J.–(BUSINESS WIRE<\/a>)–
\nCoreWeave, Inc. (Nasdaq: CRWV), The Essential Cloud for AI\u2122, today announced record-breaking results in the MLPerf\u00ae Training v6.0 benchmark suite. Running on the same CoreWeave Cloud infrastructure available to customers today, CoreWeave delivered the fastest DeepSeek-V3 671B training performance in the benchmark, training one of the most computationally demanding models ever benchmarked in 2.02 minutes on 8,192 NVIDIA GB300 NVL72 GPUs \u2014 the largest GB300 cluster submitted in this round.<\/p>\n

\nAs frontier models reach trillion-parameter scale and agentic workloads become the new standard, training performance has emerged as a defining constraint on how quickly AI teams can iterate, experiment, and bring models to production. The gap between theoretical hardware performance and real-world training efficiency is determined not by silicon alone, but by how well networking, orchestration, scheduling, storage and software work together as a system. CoreWeave’s MLPerf Training v6.0 results reflect the company’s sustained investment in full-stack optimization and the operating standard CoreWeave Mission Control<\/a> brings, consistently turning cutting-edge hardware into reliable, production-ready training performance at scale.<\/p>\n

\n“Training DeepSeek-V3 in two minutes on the largest GB300 cluster reflects years of metal-to-model engineering investment,\u201d said Chen Goldberg, Executive Vice President of Product and Engineering at CoreWeave. \u201cThese results came from the same infrastructure our customers run in production today, not a benchmark-only setup. That’s what an AI-native cloud is built to do.\u201d<\/p>\n

Training DeepSeek-V3 671B in Approximately Two Minutes: The Largest GB300 Cluster in the Benchmark<\/b><\/p>\n

\nCoreWeave submitted three GB300 NVL72 configurations on DeepSeek-V3 671B, the benchmark’s most demanding workload, achieving the fastest results across all Closed\/Available-cloud submissions. On 8,192 GPUs across 2,048 nodes, CoreWeave hit target quality in approximately two minutes. Scaling down to 4,096 GPUs across 1,024 nodes, training was completed in 3.09 minutes. At 2,048 GPUs across 512 nodes, the result was 5.54 minutes. As the cluster size doubled at each step, training time improved predictably \u2014 a consistent, near-linear scaling efficiency that reflects full-stack optimization across every layer of the CoreWeave platform.<\/p>\n

\nCoreWeave was the only submitter in the v6.0 round to scale a GB300 platform beyond 2,048 GPUs on DeepSeek-V3. The scaling story is as significant as the result demonstrating that full-stack optimization delivers more usable performance per GPU than raw scale alone. For AI teams operating under compute budgets, that scaling curve translates directly into faster training runs, shorter development cycles, and quicker time to production.<\/p>\n

Consistent Performance Across Every Cluster Size<\/b><\/p>\n

\nCoreWeave’s MLPerf Training v6.0 results demonstrate that full-stack infrastructure advantages extend across deployment sizes, not just at frontier scale.<\/p>\n

\nOn NVIDIA GB300 NVL72, CoreWeave’s 4,096-GPU deployment reached the Llama-3.1-405B reference quality target in 9.77 minutes, achieving near-parity with larger GB200 deployments while using 20% fewer GPUs. The run was built on NVIDIA NeMo Framework Release 26.04, with full CUDA graphs, Tensor\/pipeline\/context-parallel sharding tailored to the GB300 NVL72 topology, and NVIDIA Spectrum-X Ethernet running RoCE for scale-out fabric.<\/p>\n

\nOn a compact 8-node, 64-GPU NVIDIA HGX B200 cluster connected via InfiniBand, CoreWeave trained GPT-OSS-20B in 26.98 minutes and Llama-3.1-8B in 16.54 minutes. Through optimizations in orchestration, communication libraries, and distributed training configuration, CoreWeave delivered performance from the B200 platform that rivals larger and newer-generation deployments. This validated that CoreWeave’s engineering advantages benefit customers at every scale, not just the largest clusters.<\/p>\n

The Infrastructure Behind the Results<\/b><\/p>\n

\nCoreWeave’s MLPerf Training v6.0 results reflect optimizations across every layer of the stack:<\/p>\n