A technical analysis reveals that deploying the GLM-5.2 model on 8x NVIDIA B200 GPUs is most efficient using NVFP4 precision across four GPUs, rather than the more intuitive FP8 precision across all eight. This configuration, which utilizes approximately 459 GB for model weights and leaves ample space for KV cache, can achieve nearly double the throughput compared to an FP8 setup. The analysis suggests that for moderate concurrency, the model's performance is limited by memory bandwidth, making NVFP4 a more effective choice for maximizing tokens per second per dollar. AI
IMPACT Optimizing inference hardware configurations can significantly reduce costs and improve performance for large language models.
RANK_REASON Technical analysis of model deployment and hardware configuration. [lever_c_demoted from research: ic=1 ai=1.0]
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