glm-5.3 open weights and deployment

Status: Available. Z.ai published FP8 and BF16 safetensors (about 756 GB and 1.5 TB) under its own GLM-5.3 License, not the MIT licence of GLM-5.3-Flash. The licence requires a company whose group revenue exceeds US$10 billion over 12 months and that runs a model-as-a-service business to pass Z.AI's security review before commercial use; merely relaying requests to models hosted by others does not count. Z.ai does not say which precision its API serves.

Agent deployment prompt

You are my deployment agent. Deploy the official zai-org/GLM-5.3 weights as a private, OpenAI-compatible service on this host or cluster. Use the publisher's model card as the source of truth: https://huggingface.co/zai-org/GLM-5.3.

Licence
- The weights are under Z.ai's custom GLM-5.3 License, not MIT. Before downloading, show me its model-as-a-service clause and ask me to confirm that our intended use is permitted.

Preflight
- Inspect the OS, GPU model and count on every node, the interconnect between nodes, free VRAM, driver and CUDA versions, free disk space, and available ports. The FP8 checkpoint alone is about 756 GB (the BF16 repository, zai-org/GLM-5.3-BF16, is about 1.5 TB), and Z.ai publishes no minimum hardware or tensor-parallel settings; estimate whether the official weights and the requested context fit with safe headroom.
- If the hardware cannot safely run the official weights, stop before installing or launching anything. Explain the limiting resource and propose a separately reviewed alternative; do not substitute a community quantization automatically.

Deployment
- If preflight passes and I have confirmed the licence, create an isolated environment and follow the model card's SGLang or vLLM instructions and the versions they name. If a runtime asks for trust-remote-code, list the files it would execute and ask me first. Generate an API key and store it in a protected environment variable. Bind to 127.0.0.1 first; do not expose the endpoint publicly.
- The model always reasons; keep the publisher's reasoning defaults. Start with a context length the preflight supports; the model supports up to 1,048,576 tokens.

Verification and handoff
- Send a real request to /v1/chat/completions and confirm that the model responds. Report the exact launch command, installed versions, model revision, local endpoint, test result, measured GPU memory and log location. Identify any reverse-proxy or firewall changes that still need my approval. Never print the API key or other secrets.
License
GLM-5.3 License (custom; not MIT)
Hardware note
Not published by Z.ai; the FP8 weights alone are about 756 GB, and the Agent recipe performs a hardware preflight.
Recommended runtime
SGLang or vLLM (the model card also lists TokenSpeed, Transformers, KTransformers and Unsloth)
Verified date
2026-09-24

Official repository

Published model card

Parameter count
744B total, 40B active (MoE), as stated; the published weights hold about 753B parameters including the MTP layer
Context window
1M tokens (1,048,576 in the model config)
Maximum output
128K tokens on the Z.ai API
Input/output modalities
Text input; text output

Official sources