Parent tracker: #69
Design and initial evidence: #65
Summary
Build end-to-end benchmark coverage for both KV-affine Responses routing and later portable token-artifact execution across realistic agentic workloads.
Established baseline
In the initial two-replica GPT-OSS-20B previous_response_id benchmark, precise routing increased mean continuation cache hits from 49.54% to 99.08% and reduced mean continuation latency from 811.0 ms to 316.5 ms relative to load-only routing. Approximate and precise routing were effectively tied in the idle deterministic workload.
Scope
- Compare load-only, approximate-prefix, and precise Responses routing while holding prompts, ordering, vLLM processes, APC configuration, and cache state controls constant.
- Run concurrent conversations, gateway tool loops, vLLM-native tool loops, branching histories, renderer/template/tool changes, event lag, eviction pressure, restarts, and active-active EPP.
- Separate warm correct-pod hits, wrong-pod misses, HBM hits, CPU/offload loads, shared-storage loads, and full recomputation.
- Verify
/tokenize and inference prompt-token parity for every tested renderer/model family.
- Add server-side timings for agentic rehydration, llm-d render/index/score/queue, vLLM scheduler/prefill/decode, and persistence.
- After portable artifacts exist, compare full logical requests with artifact execution while holding routing and KV residency constant.
- Include GPT-OSS/Harmony plus non-Harmony Responses profiles and realistic short-, medium-, and long-context distributions.
Required output
- TTFT, total latency, input/generated/cached token counts, cache-hit ratio, selected endpoint, matched blocks, queue/load score, fallback reason, and confidence intervals.
- Raw machine-readable samples plus reproducible configuration, image/commit identities, and summarized graphs.
- Separate routing benefit from request-transfer/rendering benefit.
- Report neutral and negative results, including cases where precise and approximate routing are tied.
Acceptance criteria
- Precise routing materially outperforms load-only routing under concurrent agent/tool traffic without changing prompt token IDs or output semantics.
- Tests identify workloads where exact routing is more reliable than approximate pseudo-token identity, or explicitly conclude that no measurable difference was observed.
- Wrong-pod, eviction, restart, event-lag, and tiered-storage behavior is quantified rather than hidden in one aggregate hit metric.
- Portable artifacts preserve routing/cache-hit performance and show whether transfer/render/tokenization savings justify their operational complexity.
- Results are suitable for direct inclusion in ADR-04 and upstream project PRs.
Parent tracker: #69
Design and initial evidence: #65
Summary
Build end-to-end benchmark coverage for both KV-affine Responses routing and later portable token-artifact execution across realistic agentic workloads.
Established baseline
In the initial two-replica GPT-OSS-20B
previous_response_idbenchmark, precise routing increased mean continuation cache hits from 49.54% to 99.08% and reduced mean continuation latency from 811.0 ms to 316.5 ms relative to load-only routing. Approximate and precise routing were effectively tied in the idle deterministic workload.Scope
/tokenizeand inference prompt-token parity for every tested renderer/model family.Required output
Acceptance criteria