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` marker, and the regex was grabbing the first incomplete code block from the thinking section instead of the final answer. Fixed by splitting on ` response` and taking the last code block. 7. After the fix, coding eval showed 4/5 passes at 162 tok/s (the 1 failure was a harness truncation edge case). 8. The assistant then tried to run a full config sweep (budget=8 topk=4 window=none, budget=16 topk=4 window=2048, budget=4 topk=4 window=2048, budget=12 topk=6 window=2048) but all failed to start. The assistant started debugging why the reconfig script's readiness check was failing - the service was active but the gen endpoint wasn't responding yet. Key themes: - Sliding window attention for the drafter (compact draft cache at 2048 tokens) - Comprehensive benchmarking across context × concurrency dimensions - Coding correctness evaluation (4/5 passes after fixing extraction) - Config sweep to find optimal budget/topk/window settings - Debugging service restart and readiness check issues The chunk ends with the assistant investigating why the reconfig sweep failed, finding that the service was still loading weights when the readiness check ran. Let me write a concise 2-3 paragraph summary.</think>The assistant first verified the drafter's sliding window configuration, finding the model has `sliding_window=2048` with 5 sliding attention layers, but SGLang's draft runner was showing `draft_window_size=None`. The assistant enabled `--speculative-dflash-draft-window-size 2048` to activate the compact draft cache, which clamps the draft KV to the last 2048 tokens—matching the drafter's training window. After confirming `draft_window_size=2048, compact_cache=True` in the logs, the assistant built a comprehensive benchmark harness (`bench_ddtree_matrix.py`) testing across context lengths (60, 1024, 4096, 8192), concurrency levels (1, 8, 32, 64), and including coding correctness evaluation. The first benchmark with `budget=8 topk=4 window=2048` achieved ~170 tok/s at C=1 with short context—the best single-stream result yet—with clean scaling across the context×concurrency matrix. However, the initial coding eval showed 0/5 passes due to a code extraction bug: the thinking model outputs reasoning before a ` response` marker, and the regex was grabbing an incomplete code block from the thinking section. After fixing the extraction to split on ` response` and take the final code block, coding eval showed 4/5 passes at 162 tok/s (the single failure was a harness truncation edge case). The assistant then attempted a full config sweep across budget/topk/window combinations (8/4/none, 16/4/2048, 4/4/2048, 12/6/2048) using a reconfiguration script, but all failed to start. Debugging revealed the readiness check was racing against the service restart—the old process answered `/v1/models` briefly before being killed, while the new process was still loading weights for ~6 minutes. The chunk ends with the assistant investigating this race condition to fix the sweep automation.

The DDTree CUDA Graph Fix: Unlocking Speculative Decoding at 109 tok/s on PCIe Blackwell 2134 words

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