Segment 14
In this sub-session, the assistant successfully deployed the final gguf_loader.py and weight_utils.py patches to the container, enabling GLM-5 GGUF loading. Debugging the initial vllm serve launch revealed three issues: the speculators config crashing on glm-dsa architecture (fixed by patching the config), a torch.bfloat16 dtype incompatibility (fixed by adding --dtype float16), and most critically, the lack of a valid attention backend for Blackwell SM120 GPUs with sparse MLA. To address this, the assistant analyzed the existing Triton MLA backend and implemented a new TritonMLASparseBackend that reuses the decode kernel by treating sparse indices as a virtual block table. After deploying this backend, a subsequent launch failed due to a KeyError in weight_utils.py caused by a global string replacement bug, which was fixed by restricting replacement to the .weight suffix. With these fixes, the model loading progressed past attention backend selection, successfully selecting TRITON_MLA_SPARSE and beginning to load the 402GB model onto the 8 Blackwell GPUs.
The Final Gauntlet: Deploying GLM-5 GGUF on Blackwell GPUs Through a Cascade of Breakthroughs