Model ID
zai-org/GLM-5.1-FP8
Capabilities
GLM 5.1
GLM 5.1 is a large-scale Mixture-of-Experts (MoE) language model and a successor to GLM 5 with improved capabilities and performance, designed for long-context reasoning, coding, and agentic workflows. The model has 744B total parameters with 40B activated, using a 78-layer transformer with 6,144 hidden size and 64 attention heads. It routes tokens across 256 experts with top-8 selection for efficient sparse computation. The model supports up to ~202K context length with RoPE scaling and uses FP8 (e4m3) quantization to reduce memory and improve throughput while maintaining strong multi-step reasoning performance.
About this model
GLM 5.1 is a large-scale Mixture-of-Experts (MoE) language model and a successor to GLM 5 with improved capabilities and performance, designed for long-context reasoning, coding, and agentic workflows. The model has 744B total parameters with 40B activated, using a 78-layer transformer with 6,144 hidden size and 64 attention heads. It routes tokens across 256 experts with top-8 selection for efficient sparse computation. The model supports up to ~202K context length with RoPE scaling and uses FP8 (e4m3) quantization to reduce memory and improve throughput while maintaining strong multi-step reasoning performance.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (zai-org/GLM-5.1-FP8). Released under MIT. Further reading: Blog
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Hardware
MI325X
2,048 GB
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on MI325X · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
AMD Instinct MI325X
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