GB10

A breakthrough deployment on a 4× DGX Spark (GB10) cluster has successfully enabled GLM-5.2 with Multi-Token Prediction (MTP) speculative decoding. By reconstructing missing build recipes and pinning specific vLLM forks, developers achieved a stable 9.4 tok/s throughput, overcoming critical AWQ weight loading issues.▶ The Missing Link in Public Recipes: Existing open-source documentation for GLM-5.2 often lacks the Docker image construction layer. This successful run utilized Claude-assisted kernel reconstruction to bridge the gap between raw code and a functional production environment.▶ Dependency Fragility: The deployment highlights a strict dependency on specific vLLM versions; mismatched environments lead to immediate system crashes during AWQ weight initialization, emphasizing the need for precise environment parity.▶ Hardware-Software Synergy: By leveraging ported Sparse MLA (Multi-Head Latent Attention) Triton kernels and TP=4 configurations, the implementation maximizes the throughput capabilities of NVIDIA’s latest GB10 silicon.Bagua InsightThis case underscores the "Engineering Friction" inherent in deploying state-of-the-art models like GLM-5.2. The reliance on MTP and custom Triton kernels signals a shift in the LLM landscape: raw FLOPs are no longer enough; inference efficiency is now won in the trenches of operator optimization. The fact that developers are using LLMs (Claude) to fix the build scripts of other LLMs creates a fascinating recursive loop in AI engineering. For the industry, this proves that GLM-5.2’s architecture is viable for high-end clusters, provided the inference stack is sufficiently customized.Actionable AdviceInfrastructure teams should prioritize "Golden Image" management for GLM-series deployments, ensuring that pre-compiled Triton kernels and specific vLLM forks are baked into the CI/CD pipeline. Avoid generic inference servers; instead, invest in tuning Tensor Parallelism (TP) settings specifically for the GB10 interconnect. For those seeking maximum performance, MTP should be treated as a mandatory optimization rather than an optional feature, requiring deep integration with the underlying sparse attention mechanisms.