Segment 62
In this sub-session, the assistant shifted deployment efforts from the broken CT129 to CT200 (kpro6/dflash-train, 8× RTX PRO 6000 Blackwell) after a GPU failure. CT200 had no SGLang installed, so the assistant built a new venv, resolved a critical CUDA ABI mismatch by overlaying packages from CT129, and copied patched SGLang source files (ddtree_utils, dflash_info, dflash_worker, server_args) into place. A native SGLang DFlash service was launched on CT200 GPU1, but initial health-check issues required further troubleshooting. Once the environment was stable, the assistant enabled DDTree tree verification by adding `--speculative-ddtree-allow-hybrid-unsafe` to bypass the hybrid recurrent-layer safety gate. Through empirical tuning of the draft budget (to 15) and top-k (to 8), the assistant achieved a 24% throughput improvement over DFlash linear (124.2 vs 100.1 tok/s), with the best single-prompt result reaching 174.1 tok/s (2.1× linear). The user then requested a more extensive evaluation, prompting the assistant to design a rigorous benchmark plan covering eight speculative decoding methods, three tensor-parallel configurations (TP1/4/8), five workload types (short, long, very long, two agentic multi-turn scenarios), and a concurrency sweep, with an estimated ~2.5 hours run time on CT200's eight Blackwell GPUs. The plan includes a structured LaTeX report with pgfplots charts, establishing a reproducible methodology for evaluating speculative decoding on high-end hardware.
From Dead GPU to 24% Speedup: The DDTree Deployment Journey on Blackwell