This record-setting run used 8,192 NVIDIA GB300 NVL72 GPUs across 2,048 nodes—the largest GB300 cluster submitted in the round . CoreWeave's results were the fastest across all Closed/Available-cloud submissions, achieved through full-stack optimizations across networking, orchestration, and storage layers . The performance demonstrated effective near-linear scaling, with the same workload completing in 3.09 minutes on 4,096 GPUs and 5.54 minutes on 2,048 GPUs .

NVIDIA's Clean Sweep on Every Benchmark

NVIDIA was the only platform to submit results on every one of the seven benchmarks in this round, and it won them all—delivering the fastest time to train at scale and the highest performance on a per-accelerator basis . This continues a streak that has given NVIDIA nine times more cumulative MLPerf wins than all other submitters combined .

NVIDIA's GB300 NVL72 (Blackwell Ultra) system, which benefits from larger memory and power budgets, demonstrated substantially higher training throughput over the previous GB200 NVL72. At the same scale, the GB300 NVL72 delivered up to 1.6x faster training than its predecessor . Beyond raw hardware, NVIDIA's continuous software improvements played a critical part: training throughput for DeepSeek-V3 improved by 1.3x in just three months on identical hardware, driven by innovations like full-iteration CUDA graphs and CuTe DSL fusions .

Key Training Times Across Benchmarks

The combination of the NVLink-switch domain and the scale-out Spectrum-X Ethernet fabric enabled NVIDIA's partners to achieve record times across all workloads :

• Llama 3.1 8B Pretraining: 5.2 minutes
• Llama 2 70B LoRA Fine-Tuning: 0.40 minutes
• Image Generation (FLUX.1): 12.5 minutes
• Recommender (DLRM-DCNv2): 0.71 minutes
• Graph Neural Network (R-GAT): 0.84 minutes
• Object Detection (RetinaNet): 1.4 minutes

GB300 NVL72 vs. GB200 NVL72: The Performance Leap

The generational uplift from Blackwell to Blackwell Ultra is pronounced. In inference workloads, the GB300 NVL72 delivers up to 2.77x higher token-per-second throughput compared to the GB200 NVL72 . This leap is attributed not only to architectural silicon gains but also to the aggressive software-hardware co-design that has become a hallmark of the Blackwell platform. The combination of these two systems allows data center operators to choose configurations optimized for specific cost, throughput, and latency requirements.

New MoE Workloads Reflect Industry Reality

For the first time, MLPerf Training introduced benchmarks that mirror the architecture of the most advanced frontier models in production today. Alongside the massive DeepSeek-V3 671B workload, a smaller GPT-OSS-20B MoE pretraining benchmark was added . NVIDIA was the only platform to submit results on both new MoE workloads, using the GB300 NVL72 system with custom software stacks, CUDA graphs, and advanced MoE routing to optimize the sparse, bursty communication patterns inherent to these models .

Record Participation and a More Diverse Ecosystem

The v6.0 round saw its broadest participation yet, with 24 organizations submitting results across 95 distinct systems using 13 different hardware accelerators . The technical diversity extended beyond just hardware to include multiple precision recipes, such as NVFP4 from NVIDIA and MXFP4 from AMD. AMD, in particular, demonstrated competitive performance with its Instinct MI355X GPU, coming within 5% of NVIDIA's B200 platform on Llama 2-70B fine-tuning and within 6% on Llama 3.1-8B pretraining—an important sign of a developing competitive landscape .

The Underlying Network: Scaling to 8,192 GPUs

The ability to scale a single training job to thousands of GPUs is critical for MoE models, which feature bursty all-to-all communication as tokens are dynamically routed to different experts. To handle this, NVIDIA's partners leveraged Spectrum-X Ethernet with Adaptive Routing and Congestion Control, sustaining near-theoretical fabric bandwidth across 8,192 Blackwell GPUs in hyperscale clusters . This networking stack is as vital as raw GPU compute in turning hardware specifications into benchmark-topping results and, ultimately, into faster training for real-world AI factories.

This record-setting run used 8,192 NVIDIA GB300 NVL72 GPUs across 2,048 nodes—the largest GB300 cluster submitted in the round . CoreWeave's results were the fastest across all Closed/Available-cloud submissions, achieved through full-stack optimizations across networking, orchestration, and storage layers . The performance demonstrated effective near-linear scaling, with the same workload completing in 3.09 minutes on 4,096 GPUs and 5.54 minutes on 2,048 GPUs .

NVIDIA's Clean Sweep on Every Benchmark

NVIDIA was the only platform to submit results on every one of the seven benchmarks in this round, and it won them all—delivering the fastest time to train at scale and the highest performance on a per-accelerator basis . This continues a streak that has given NVIDIA nine times more cumulative MLPerf wins than all other submitters combined .

NVIDIA's GB300 NVL72 (Blackwell Ultra) system, which benefits from larger memory and power budgets, demonstrated substantially higher training throughput over the previous GB200 NVL72. At the same scale, the GB300 NVL72 delivered up to 1.6x faster training than its predecessor . Beyond raw hardware, NVIDIA's continuous software improvements played a critical part: training throughput for DeepSeek-V3 improved by 1.3x in just three months on identical hardware, driven by innovations like full-iteration CUDA graphs and CuTe DSL fusions .

Key Training Times Across Benchmarks

The combination of the NVLink-switch domain and the scale-out Spectrum-X Ethernet fabric enabled NVIDIA's partners to achieve record times across all workloads :

• Llama 3.1 8B Pretraining: 5.2 minutes
• Llama 2 70B LoRA Fine-Tuning: 0.40 minutes
• Image Generation (FLUX.1): 12.5 minutes
• Recommender (DLRM-DCNv2): 0.71 minutes
• Graph Neural Network (R-GAT): 0.84 minutes
• Object Detection (RetinaNet): 1.4 minutes

GB300 NVL72 vs. GB200 NVL72: The Performance Leap

The generational uplift from Blackwell to Blackwell Ultra is pronounced. In inference workloads, the GB300 NVL72 delivers up to 2.77x higher token-per-second throughput compared to the GB200 NVL72 . This leap is attributed not only to architectural silicon gains but also to the aggressive software-hardware co-design that has become a hallmark of the Blackwell platform. The combination of these two systems allows data center operators to choose configurations optimized for specific cost, throughput, and latency requirements.

New MoE Workloads Reflect Industry Reality

For the first time, MLPerf Training introduced benchmarks that mirror the architecture of the most advanced frontier models in production today. Alongside the massive DeepSeek-V3 671B workload, a smaller GPT-OSS-20B MoE pretraining benchmark was added . NVIDIA was the only platform to submit results on both new MoE workloads, using the GB300 NVL72 system with custom software stacks, CUDA graphs, and advanced MoE routing to optimize the sparse, bursty communication patterns inherent to these models .

Record Participation and a More Diverse Ecosystem

The v6.0 round saw its broadest participation yet, with 24 organizations submitting results across 95 distinct systems using 13 different hardware accelerators . The technical diversity extended beyond just hardware to include multiple precision recipes, such as NVFP4 from NVIDIA and MXFP4 from AMD. AMD, in particular, demonstrated competitive performance with its Instinct MI355X GPU, coming within 5% of NVIDIA's B200 platform on Llama 2-70B fine-tuning and within 6% on Llama 3.1-8B pretraining—an important sign of a developing competitive landscape .

The Underlying Network: Scaling to 8,192 GPUs

The ability to scale a single training job to thousands of GPUs is critical for MoE models, which feature bursty all-to-all communication as tokens are dynamically routed to different experts. To handle this, NVIDIA's partners leveraged Spectrum-X Ethernet with Adaptive Routing and Congestion Control, sustaining near-theoretical fabric bandwidth across 8,192 Blackwell GPUs in hyperscale clusters . This networking stack is as vital as raw GPU compute in turning hardware specifications into benchmark-topping results and, ultimately, into faster training for real-world AI factories.