Unverified50% confidenceFactExact time
Across batch sizes from 8 to 256, vLLM's decoding performance is on par with DeepInfer.
1
Sources
50%
Confidence
Short-term (~14 days)
Relevance
10/29/2026
First Seen
Valid until: 11/12/2026
Sources
Related Entities
Related Claims
UnverifiedThe decode unit of DeepSeek V3 uses 144-card EP, amortizing the weight load overhead across large batches and freeing up GPU memory to increase batch size.68% similarUnverifiedIn the DeepSeek V1 era, each incremental token required nearly 400 KB of KV cache, which dropped to 48 KB in V3.2.66% similarUnverifiedOne B70 user running vLLM + GPT-Q INT4 quantization with MTP depth 4 achieved 84.6 tokens/s on a 27B model, but this only supports single-stream and uses different quantization, making direct comparison impossible.66% similarUnverifiedThe author reports that under the same model and the same decoding settings, dlmrt is 1.2 to 1.6 times faster than SGLang/vLLM on a single H100.65% similar
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