Model ID
zai-org/GLM-5-FP8
Capabilities
GLM 5
GLM 5 is a large Mixture-of-Experts (MoE) language model developed by Z.ai for complex reasoning, coding, and long-horizon agentic workflows. The model features a 744B parameter architecture with 40B activated parameters, built on a 78-layer transformer with 64 attention heads and a 6,144 hidden size, utilizing 256 routed experts with 8 experts activated per token. It integrates DeepSeek Sparse Attention (DSA) to reduce deployment cost while maintaining performance, and supports a ~202K token context window for large-scale multi-step reasoning tasks.
About this model
GLM 5 is a large Mixture-of-Experts (MoE) language model developed by Z.ai for complex reasoning, coding, and long-horizon agentic workflows. The model features a 744B parameter architecture with 40B activated parameters, built on a 78-layer transformer with 64 attention heads and a 6,144 hidden size, utilizing 256 routed experts with 8 experts activated per token. It integrates DeepSeek Sparse Attention (DSA) to reduce deployment cost while maintaining performance, and supports a ~202K token context window for large-scale multi-step reasoning tasks.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (zai-org/GLM-5-FP8). Released under MIT. Further reading: Paper · 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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