{"type":"post","id":"improve-ttft-for-multi-turn-agentic-workloads-with-207bc9","slug":"improve-ttft-for-multi-turn-agentic-workloads-with-207bc9","title":"Improve TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM","description":"**Thesis:** In disaggregated multi-turn inference, returning KV state from decode to prefill can reduce repeated context computation, but the benefit depends on cache retention and workload shape.","retrieval_nugget":"**Thesis:** In disaggregated multi-turn inference, returning KV state from decode to prefill can reduce repeated context computation, but the benefit depends on cache retention and workload shape.","published_at":"2026-10-19","updated_at":"2026-10-19","record_date":"2026-10-19","date_kind":"published_at","topics":["inference"],"source_urls":["https://aws.amazon.com/blogs/machine-learning/improve-ttft-for-multi-turn-agentic-workloads-with-bi-directional-kv-transfer-in-vllm"],"schema_version":"newruntime-agent-readable-v0.2","stable_id":"post:improve-ttft-for-multi-turn-agentic-workloads-with-207bc9","status":"published","visuals":[],"editorial_provenance":{"schema_version":"newruntime-editorial-copy-v1","content_status":"source_grounded_final","final_copy_sha256":"sha256:4815c56ef84e8dd180852617d22894366395df2627657fadd03a302d5fcd524e","reviewed_at":"2026-10-19T20:07:41.592Z","source_evidence_count":1,"verified_claim_count":2,"site_analysis_schema_version":"newruntime-site-analysis-v1","site_object_kind":"field_note","observed_fact_count":2,"implication_count":1,"watch_condition_count":1,"related_record_count":0},"analysis":{"schema_version":"newruntime-site-analysis-v1","object_kind":"field_note","thesis":"In disaggregated multi-turn inference, returning KV state from decode to prefill can reduce repeated context computation, but the benefit depends on cache retention and workload shape.","observed_facts":[{"text":"AWS says conventional prefill-decode serving transfers KV cache only from prefill to decode, leaving prefill to recompute some prior response tokens on later turns.","source_urls":["https://aws.amazon.com/blogs/machine-learning/improve-ttft-for-multi-turn-agentic-workloads-with-bi-directional-kv-transfer-in-vllm"]},{"text":"AWS reports up to 1.6x TTFT reduction for Qwen3-32B and up to 3.3x for GLM-5-FP8 on a 1P1D Amazon EC2 P5en deployment with EFA.","source_urls":["https://aws.amazon.com/blogs/machine-learning/improve-ttft-for-multi-turn-agentic-workloads-with-bi-directional-kv-transfer-in-vllm"]}],"mechanism":"The described bi-directional transfer returns previously computed KV blocks from decode to prefill on subsequent turns, coordinated by a KV-cache-aware router and governed by a configurable recompute threshold.","why_now":"Multi-turn agent interactions accumulate tool results and model responses, so repeated prefill work can grow alongside latency and accelerator use.","implications":["Teams using disaggregated serving should evaluate whether reverse KV transfer reduces repeated prefill work under their actual context lengths, cache retention settings, and routing behavior."],"evidence_boundary":"The evidence is a vendor engineering post and its described benchmarks, not an independently reproduced comparison across hardware or traffic patterns.","watch_conditions":["Look for independent measurements across different context lengths and cache-miss rates that report both TTFT and accelerator utilization."],"related_records":[],"new_branch_reason":"No prior New Runtime analytical record was supplied; this is a distinct serving-mechanism and benchmark record."},"editor":{"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]},"routes":{"html":"https://newruntime.com/posts/improve-ttft-for-multi-turn-agentic-workloads-with-207bc9/","markdown":"https://newruntime.com/posts/improve-ttft-for-multi-turn-agentic-workloads-with-207bc9.md","json":"https://newruntime.com/posts/improve-ttft-for-multi-turn-agentic-workloads-with-207bc9.json"},"next_reads":[{"type":"topic","path":"/topics/inference/","url":"https://newruntime.com/topics/inference/","title":"Inference","reason":"Explore the inference topic hub.","media_type":"text/html","markdown_url":"https://newruntime.com/topics/inference.md","json_url":"https://newruntime.com/topics/inference.json"},{"type":"collection","path":"/posts/","url":"https://newruntime.com/posts/","title":"Field Notes","reason":"Open the latest editorial analysis.","media_type":"text/html","json_url":"https://newruntime.com/posts.json"},{"type":"collection","path":"/source-ledger.json","url":"https://newruntime.com/source-ledger.json","title":"Source Ledger","reason":"Inspect the public source evidence graph.","media_type":"application/json"}]}