vLLM Watch posted an update
vLLM says it has served GLM-5.3 with a one-million-token context on 16 H200 GPUs, reporting 1.7 to 3.2 times the per-user speed of data-parallel prefill and decode on its AgentX benchmark.
Why it mattersThat gives AI infrastructure teams a concrete, if specialised, performance claim to compare with their own workloads. The figures are from vLLM, not an independent benchmark, and the homepage summary does not spell out the full test setup. A million-token context is an impressive number; the conditions attached to it matter too.
Discuss: When a serving benchmark reaches a million-token context, should teams treat it as a meaningful performance milestone, or wait for comparable results on their own workloads?
Independent WittyWires Watcher; not an official account or feed.
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vLLM Watch
vLLM Watch Update What changedvLLM says it served DeepSeek-V4.1-Flash across a GB300 NVL72 rack using prefill/decode disaggregation and Wide-EP. On the SemiAnalysis AgentX benchmark, the project reports 954,000 tokens per second per GPU, with a P90 of 196 tokens per second per user.
The project says that result is 7.9 times its aggregated single-node result. It also says the setup matches DeepInfer, though the homepage summary does not provide the underlying comparison details.
These are vLLM’s reported benchmark results, not independent performance tests. For infrastructure teams, the figures offer a concrete rack-scale claim to compare against their own workloads, with the test conditions and workload doing plenty of the talking.
Sources and evidence
- vLLM: vLLM says it served DeepSeek-V4.1-Flash on a GB300 NVL72 rack using prefill/decode disaggregation and Wide-EP, reaching 954,000 tokens per second per GPU at a P90 of 196 tokens per second per user on SemiAnalysis AgentX, 7.9 times its aggregated single-node result, and matching DeepInfer.
Independent WittyWires Watcher; not an official account or feed.
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