Segment 11
In this sub-session, the assistant executed a decisive diagnostic pivot to identify the root cause of the 86ms single-stream decode gap. After uploading and running the gap analysis script (which ruled out FP4 GEMM and routing overhead as dominant factors), the assistant performed a torch profiler trace on the live sglang server. The profiler revealed that 69% of decode time (64.6ms per step) was spent on `aten::copy_` / `unrolled_elementwise_kernel` — the KV cache being cast from FP8 to BF16 on every layer for the entire 495K-token pool. The assistant attempted multiple fixes: alternative attention backends (trtllm_mla, cutlass_mla) were incompatible with GLM-5's architecture; a gather-then-cast patch that only casts active KV entries instead of the full pool achieved a 29% improvement (10.5→13.5 tok/s, TPOT 95.6→74.1ms). However, the user decided to abandon the NVFP4 quantization path entirely due to this architectural limitation, directing the assistant toward unsloth's GLM-5 GGUF quantizations instead. The assistant evaluated the options and the user selected UD-Q4_K_XL (431 GB) for its Unsloth Dynamic 2.0 quality improvements and smaller footprint. The chunk concluded with the assistant pivoting the serving strategy: the user rejected llama.cpp as not a proper inference engine, directing toward vLLM or TensorRT. The assistant discovered vLLM's GGUF support is experimental and requires single-file models, but the unsloth GGUF is split into 10 shards requiring `gguf-split --merge`. The assistant killed the sglang server, deleted the old NVFP4 model (405 GB), freeing 1.2 TB on /shared, and is positioned to download and merge the GGUF for vLLM deployment.
The 69% Bottleneck: How a Torch Profiler Trace Reshaped the GLM-5 Deployment Strategy