AI Cloud Firm CoreWeave Claims Leading DeepSeek-R1 Throughput in New MLPerf 0.7 Endpoints Benchmark
CoreWeave self-reported 441k tokens/sec system throughput for DeepSeek-R1 inference in the inaugural MLPerf Endpoints benchmark, emphasizing production-environment validation.
US-based AI cloud infrastructure company CoreWeave announced on July 28, 2026, via its official blog that it achieved leading results in the inaugural MLPerf 0.7 Endpoints benchmark by MLCommons. Running the frontier DeepSeek-R1 reasoning model on a NVIDIA GB200 NVL72 system with 68 NVIDIA Blackwell GPUs, CoreWeave reported a sustained system throughput of 441,740 output tokens per second at 16,384 concurrent requests, and up to 6,496 tokens per second per GPU.
MLPerf Endpoints is MLCommons' newest benchmark suite, designed to measure how an inference service behaves under realistic, concurrent, online load, reporting the full throughput-versus-latency curve rather than a single data point. The test includes DeepSeek-R1, a 671-billion-parameter mixture-of-experts reasoning model that emits thousands of tokens per response, making it the hardest serving workload in the suite.
CoreWeave emphasized that its results were validated on standard production clusters using the same network fabric, scheduler, storage, and Mission Control orchestration that customers use, rather than an isolated environment tuned for the test. The company noted that throughput per GPU is the metric most directly reflecting the economics of real inference services, as it maps to tokens served per accelerator and cost per GPU.
It should be noted that these performance figures are self-reported by CoreWeave. While the submission process was verified by the MLCommons Association, independent third-party reproduction has not been publicly reported. Additionally, the 6,496 tokens per second per GPU figure is derived by dividing system TPS by the GPU count (68) and is not a direct MLPerf reported metric.