Optimal Deployment Guide for GLM-5.2 on Blackwell

An analysis has emerged showing that when serving the GLM-5.2 model on NVIDIA B200 SXM nodes, leveraging NVFP4 quantization along with Replicas is far more efficient than simply scaling Tensor Parallelism (TP).

Key Analysis:

• Characteristics of the MoE Architecture: GLM-5.2 is an MoE model with only 40B parameters activated out of approximately 750B total parameters, giving it a Bandwidth-bound characteristic where inference performance is determined more by memory bandwidth than by compute.
• Benefits of NVFP4: It halves the weight data size compared to FP8, reducing the memory read load, which is the key driver of the performance improvement over H200.
• Optimal Configuration (TP=4 vs TP=8):
  • TP=8 (FP8): Uses the entire node as a single engine, with performance rising gradually as concurrency increases.
  • TP=4 (NVFP4): Allows a single node to be split into two independent engines (TP=4 x 2), recording roughly 2x higher throughput compared to the FP8 TP=8 configuration.

Expected Performance (based on 8K Context):

• With the NVFP4 (TP=4) configuration, at a concurrency of 64, it records 3,740 tok/s/GPU, showing overwhelmingly greater efficiency than the FP8 TP=8 configuration (1,619 tok/s/GPU).