Chunk 65.2

The assistant definitively diagnosed the severe throughput regression (32 t/s) the user observed, ruling out the parser change and output length as causes. Through controlled experiments, it isolated the root cause to two compounding effects inherent to the C=1 speculative decoding stack: the per-step verify forward pass is attention-bound and its latency grows significantly with context length (34ms at 13 tokens to 144ms at 5.5k tokens), and the undertrained drafter achieves a very low acceptance rate (~2.9 tokens/step) on hard reasoning/analysis text compared to predictable text (~7-8 tokens/step). The measured step time at the user's context length combined with the low acceptance rate mathematically produces exactly the observed 32 t/s, confirming the system is behaving as expected for this workload, not suffering from a bug. Responding to the user's request to set the maximum context length to 200k, the assistant verified that the Kimi K2.6 model supports up to 262,144 tokens via YaRN scaling, while the service was capped at 32,768. A memory analysis revealed that the current KV cache pool of ~101k tokens uses only a fraction of the available GPU memory (~10GB free per GPU), as MLA's per-token KV overhead is exceptionally low (~8.6KB). Concluding that the increase is memory-feasible, the assistant modified the systemd service file to set `--context-length 200000` and issued a service restart, though the user aborted the subsequent readiness polling loop before it completed. The overarching themes of this chunk are rigorous performance diagnosis through controlled measurement, separating genuine system behavior from perceived regressions, and balancing the model's theoretical capabilities against the practical performance constraints of the current inference stack. The assistant committed the diagnostic tools and provided a clear roadmap of levers for improvement, with the dominant fix being a better-trained drafter.

The Three Acts of Engineering: Diagnosis, Extension, and Analytical Pivot in a Speculative Decoding Stack 3047 words

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