Model ID
zai-org/GLM-5.3
Capabilities
GLM 5.3
GLM 5.3 is an advanced Mixture-of-Experts model designed for long-horizon reasoning and large-scale coding, delivering substantially improved performance over GLM-5.2 through post-training. Its architecture features 78 layers with hybrid linear and DeepSeek sparse attention, 64 attention heads, 64 KV heads, and 256 routed experts with 8 activated per token. The model incorporates IndexShare sparse attention to improve long-context efficiency and an enhanced multi-token prediction layer for faster speculative decoding. Supporting up to a 1M token context window, it is optimized for complex reasoning, extended coding workflows, and sustained long-context tasks.
About this model
GLM 5.3 is an advanced Mixture-of-Experts model designed for long-horizon reasoning and large-scale coding, delivering substantially improved performance over GLM-5.2 through post-training. Its architecture features 78 layers with hybrid linear and DeepSeek sparse attention, 64 attention heads, 64 KV heads, and 256 routed experts with 8 activated per token. The model incorporates IndexShare sparse attention to improve long-context efficiency and an enhanced multi-token prediction layer for faster speculative decoding. Supporting up to a 1M token context window, it is optimized for complex reasoning, extended coding workflows, and sustained long-context tasks.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (zai-org/GLM-5.3). Released under GLM 5.3. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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