A GPUStack community user has published a hands-on benchmark of GLM-5.2-FP8-DSpark, a variant of the GLM-5.2 model enhanced with speculative decoding via an external draft model from RedHatAI. The test measures inference speedup when using the same main model weights with an additional small draft model. Early results indicate significant latency reduction for token generation, making this approach attractive for self-hosted LLM deployments. The post details the setup, including model loading and inference configuration on GPUStack. For teams running Chinese LLMs like GLM on their own hardware, speculative decoding offers a practical path to faster responses without changing the core model. This benchmark provides a useful reference point for MLOps engineers evaluating inference optimization techniques.
A community user tested GLM-5.2-FP8-DSpark on GPUStack, using a draft model for speculative decoding. The post shares practical speedup numbers and configuration details. This matters for teams running Chinese LLMs on their own GPU infrastructure.