Model ID
zai-org/GLM-5.3-Flash
Capabilities
GLM 5.3 Flash
GLM 5.3 Flash is a natively multimodal model designed for efficient, high-performance agentic workloads, coding, and long-context applications. It has 320B total parameters with 18B active parameters, delivering better performance than GLM 5.2 while substantially reducing serving costs. The model uses a hybrid sparse architecture combining linear attention with DeepSeek-style sparse attention, alongside 288 routed experts, 8 activated experts per token, and Manifold-Constrained Hyper-Connections (mHC) for improved scaling efficiency. It supports up to 1M tokens context window and multimodal capabilities for demanding agentic workloads.
About this model
GLM 5.3 Flash is a natively multimodal model designed for efficient, high-performance agentic workloads, coding, and long-context applications. It has 320B total parameters with 18B active parameters, delivering better performance than GLM 5.2 while substantially reducing serving costs. The model uses a hybrid sparse architecture combining linear attention with DeepSeek-style sparse attention, alongside 288 routed experts, 8 activated experts per token, and Manifold-Constrained Hyper-Connections (mHC) for improved scaling efficiency. It supports up to 1M tokens context window and multimodal capabilities for demanding agentic workloads.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (zai-org/GLM-5.3-Flash). Released under MIT. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Requires vllm/vllm-openai:glm53-flash or any standard vLLM image v0.27.0+ to support this model configuration.
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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