Overview
GLM-5.2 is a 753.3B-parameter open-weight language model from zai-org (Z.ai / Zhipu AI). It uses a mixture-of-experts design, tagged glm_moe_dsa, so only a fraction of those parameters fire on any given token. The model handles English and Chinese, carries a 1,048,576-token context window, and is built for thinking, reasoning, and code.
The local angle is the point of running it through Atomic Chat. The weights are public on Hugging Face, so once they are on your machine the model runs on your own hardware, offline, with nothing sent to an external API. Your prompts and code stay on the device.
What it is good at
GLM-5.2 leans toward agentic coding and long-horizon work. These are the tasks it fits.
- Code generation and review — it writes implementations from a spec and reasons across multiple files, and ranks near the top of open-weight coding benchmarks like SWE-bench Pro and Terminal-Bench.
- Long-document reasoning — the 1,048,576-token window holds an entire codebase or a long technical document in one pass, so you can ask questions across the whole thing.
- Step-by-step problem solving — the
thinkingcapability lets it work through multi-step logic and agentic tasks before committing to an answer.
Running it locally
At 753.3B parameters this is a heavy model. Full BF16 weights need hundreds of gigabytes, so most local users run a quantized build: a 2-bit dynamic quant lands around 241 GB and fits a 256 GB unified-memory Mac or a 24 GB GPU paired with 256 GB of system RAM and MoE offloading. The full 1,048,576-token context is available if your memory can hold the KV cache.
huggingface-cli download zai-org/GLM-5.2
From there you can serve it with vLLM or run a GGUF quant through llama.cpp, or load it in Atomic Chat with one click and start a private offline session.
License
GLM-5.2 ships under the MIT license. That permits commercial use, modification, redistribution, and fine-tuning the weights on your own code or domain data, as long as the license notice stays attached.