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AI CLUSTERED EVENT · 10/14/2026

vLLM Introduces Push-Based KV Transfer to Optimize TTFT and TPOT in Disaggregated Inference

1 reports archived1 independent sourcesupdated 10/14/2026, 08:00:00
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vLLM introduces a push-based KV cache transfer strategy within its disaggregated prefill-decode architecture to mitigate cross-node data movement latency. By leveraging the NIXL connector over RDMA networks, this approach significantly improves Time-to-First-Token (TTFT) and Time-Per-Output-Token (TPOT). The update enhances resource utilization and response speed for large-scale LLM serving by optimizing low-level network scheduling.

LATEST/vLLM introduces a push-based KV cache transfer strategy within its disaggregated prefill-decode architecture to mitigate cross-node data movement latency. By leveraging the NIXL connector over RDMA networks, this approach significantly improves Time-to-First-Token (TTFT) and Time-Per-Output-Token (TPOT). The update enhances resource utilization and response speed for large-scale LLM serving by optimizing low-level network scheduling.

HEAT TRENDHourly heat curve

19 fully observed hours
Now
13.0
Peak
20.008:00
24h change
-35%

The line compares only sources observed throughout; gap hours are interpolated to keep the trend continuous.

TIMELINECoverage timeline

Total 1 reports · Latest first
  1. vLLM 官方博客T1·68 pts
    • vLLM supports disaggregating Prefill and Decode stages to independently optimize compute and memory resources.
    • The new Push-Based KV transfer mechanism efficiently moves KV cache blocks over RDMA networks via the NIXL connector.
    • This optimization directly addresses network bottlenecks in disaggregated inference, significantly reducing TTFT and TPOT metrics.
vLLM Introduces Push-Based KV Transfer to Optimize TTFT and TPOT in Disaggregated Inference | Today for AI