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Author

Sunita Nadampalli

2 public articles
Artificial intelligence

Push-based KV transfer to improve TTFT and TPOT for disaggregated inference in vLLM

SSunita Nadampalli·Oct 20, 2026, 12:05 AM·9 min read

The gist: vLLM's prefill-decode disaggregation splits LLM inference across engine instances but must transfer the KV cache between them. This post introduces a push-based NIXL connector that moves transfer coordination off the critical path, delivering up to 3x better TTFT and 30% better TPOT in benchmarks on Amazon EC2 P5en ins

Artificial intelligence

Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM

SSunita Nadampalli·Oct 20, 2026, 12:03 AM·9 min read

The gist: Multi-turn agentic workloads reprocess growing conversation context each turn, driving up latency. See how bi-directional KV cache transfer lets prefill and decode nodes reuse previously computed context across turns, cutting time to first token by up to 1.6x for Qwen3-32B and 3.3x for GLM-5-FP8 on Amazon EC2 P5en inst

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