You can run a GLM model locally because the weights are genuinely published, not gated behind a request form or a hosted endpoint. This page covers the four things that decide whether a local deployment works: the licence you are operating under, where the weights live, which serving frameworks are supported, and how to size hardware for the context length you actually need rather than for the parameter count alone. None of them are hard individually; getting all four right at once is what separates a working deployment from a weekend of debugging.
The reason to bother is straightforward. An open-source GLM model running on your own hardware keeps every prompt inside your network, cannot be deprecated out from under you, and can be fine-tuned on data you would never send to a third party. Those properties are why teams choose open weights even when a hosted model scores a few points higher.
Core GLM model weights are released under an MIT license with no regional restrictions. MIT is about as permissive as software licensing gets: use, modify, distribute and deploy commercially, with attribution and no warranty. There is no separate acceptable-use rider narrowing what the licence otherwise grants, and no geography clause deciding where you may serve it.
That matters more for models than for ordinary software, because many nominally open releases carry riders that make production deployment legally ambiguous. Confirm the licence file shipped with the specific checkpoint you download — it is the authoritative text, and it takes about thirty seconds to read.
The weights are published on HuggingFace and on ModelScope. Both host the full checkpoints rather than adapters or partial releases, so you can pull them with the standard tooling for either platform. Mirroring the checkpoint into your own artefact store before rollout is good practice for the same reason it is with container images: your deployment should not depend on an external host being reachable at the moment you scale up.
The model authors list support for five inference stacks, which between them cover essentially every local deployment shape from a single workstation to a multi-node cluster.
The reference implementation. Slowest for production serving, but the easiest way to confirm a checkpoint loads and generates correctly before you invest in a faster stack. Start here when debugging.
The usual production default. Paged attention and continuous batching make it strong under concurrent load, and it is generally the fastest route from a downloaded checkpoint to an OpenAI-compatible endpoint on your own hardware.
Optimised for structured generation and heavy prefix reuse. Worth choosing when many requests share a long system prompt or a common document prefix, which is exactly the shape of most agent workloads.
An additional supported serving path listed by the model authors, useful where its deployment characteristics fit your infrastructure better than the alternatives.
Targets heterogeneous CPU-and-GPU inference, which is what makes it interesting for constrained hardware: it can offload parts of the model to system memory and keep a large model usable on a machine that could not otherwise hold it.
The most common local-deployment mistake is budgeting memory for weights and forgetting the KV cache. Parameter count sets a floor, but the cache scales with context length and concurrency, and at long context it dominates. This is the same wall the model authors hit when serving the flagship: past a few hundred thousand tokens the bottleneck stops being compute and becomes cache capacity, kernel efficiency and CPU overhead.
The practical consequence is that you should size for your real context distribution, not for the maximum the model supports. Running GLM-5.2 with a 128K window is a very different hardware proposition from running it at the full million, and most workloads never approach the ceiling. If your hardware is tight, the lightweight GLM-4.7-Flash and GLM-4.5-Air tiers, or the 200K-context GLM-5.1, will fit where the flagship will not.
Match the model to the machine. On a single workstation accelerator, the 30B GLM-4.7-Flash or GLM-4.5-Air are the realistic options and are perfectly capable for classification, extraction, summarisation and routine code edits. On a serious multi-GPU node, GLM-5.1 gives you a 200,000-token window at a much smaller cache cost than the flagship. On a cluster, GLM-5.2 gives you the full 1M context, explicit effort levels and the strongest published benchmark column in the family. For speech-to-text rather than generation, GLM-ASR runs the transcription stage under the same licence.
All figures on this page are results as published by the model authors. glmmodel.com reports them; it does not run these evaluations.
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