Unverified50% confidenceBenchmarkExact time
The SWA (Sliding Window Attention) bounded replay feature introduced in DeepSeek models has been fully implemented by vLLM, and official data shows TTFT drops by about 30%.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/13/2026
First Seen
Valid until: 1/11/2027
Sources
Related Entities
Related Claims
UnverifiedIn DeepSeek-V4 optimizations, skipping unnecessary topk/router during the decoding phase brings a 3.4% end-to-end TTFT improvement, and workspace reuse brings a 3.9% TTFT improvement.66% similarUnverified使用DeepSpec配合vLLM启动可使单次推理成本减半66% similarUnverifiedDeepSeek-V4's sparse MLA (Multi-head Latent Attention) has achieved end-to-end availability in vLLM v0.28.0, supporting three scenarios: normal decoding, MTP, and DSpark.66% similarUnverifiedCurrently, optimization projects for Ascend in frameworks such as vLLM have not yet integrated DeepGEMM.64% similarUnverifiedvLLM v0.3.1 delivers deep optimizations for the DeepSeek series of models, with inference accelerated 1.9x in low-concurrency scenarios and throughput increased 5.3x in high-concurrency scenarios.61% similar
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