GLM-4.5: what matters beyond the model card
Z.ai positions GLM-4.5 around reasoning, coding and intelligent-agent use. The model card identifies 355B total parameters, 32B active parameters and MIT licensing, while the configuration exposes a 131,072-token maximum position length. That makes GLM-4.5 particularly relevant to OWM’s thesis that open-weight sovereignty is not synonymous with local laptops: it can also mean choice across data-center providers.
GLM-4.5 is a strong example of open-weight competition moving into agentic infrastructure. Its 355B total / 32B active architecture is far beyond workstation scale, but the MIT license and support in Transformers, vLLM and SGLang make the model layer comparatively portable for organizations already operating distributed inference.
Model facts
Runtime paths recorded by OWM: Transformers · vLLM · SGLang · Docker Model Runner. Runtime support is version-sensitive; a named runtime should not be read as a guarantee that every quantization, context size or feature works identically.
Why this model matters
Z.ai positions GLM-4.5 around reasoning, coding and intelligent-agent use. The model card identifies 355B total parameters, 32B active parameters and MIT licensing, while the configuration exposes a 131,072-token maximum position length. That makes GLM-4.5 particularly relevant to OWM’s thesis that open-weight sovereignty is not synonymous with local laptops: it can also mean choice across data-center providers.
OWM evaluates a model as infrastructure, not only as a benchmark entry. That means the exact checkpoint, license, runtime ecosystem, memory footprint, ability to move between providers and the quality of the evidence all matter alongside model capability.
Hardware reality
The checkpoint is 355B total parameters. A simplistic 16-bit raw-weight estimate is roughly 710 GB before overhead, so distributed inference is the realistic default. An FP8 variant is published, but OWM still treats exact hardware sizing as configuration-specific rather than presenting one universal GPU count.
OWM deliberately separates raw-weight arithmetic, publisher guidance and measured runtime evidence. A model that theoretically fits into a memory budget can still fail in practice because of KV cache, runtime buffers, vision components, tensor-parallel overhead or concurrent requests.
License reality
Z.ai states that GLM-4.5 is released under MIT and can be used commercially and for secondary development. That is a notable advantage at this scale because it separates a frontier-sized agentic model from a custom commercial model license.
This is an informational deployment summary, not legal advice. Production users should review the exact current license, usage policy, derivative-model terms and applicable law before shipping a product.
OWM Sovereignty Lens
OWM does not assign a single sovereignty score. We describe the layers separately because a model can be highly portable technically while remaining conditional legally — or permissively licensed while requiring infrastructure that limits practical choice.
Runtime evidence
OWM currently records source-verified runtime support. This page deliberately avoids inventing a single speed figure for a model whose performance changes dramatically with GPU topology, precision, batching and interconnect.
“Third-party measured” means the result was measured outside Open Weight Models and is shown with provenance. “OWM runtime tested” is reserved for configurations that OWM physically reproduces with an exact checkpoint, runtime version, hardware configuration, workload and date.
Change history
First OWM verification snapshot created. From this date forward, material changes to the model card, license, checkpoints, runtime support and deployment facts can be appended without reconstructing unobserved history.
Open the global OWM Change History →
Where GLM-4.5 fits — and where it does not
Where it fits
- Large-scale agentic reasoning and coding infrastructure.
- Organizations with multi-GPU capacity that want a permissive model license.
- Provider-hosted deployments where the checkpoint should remain portable between inference vendors.
- Teams standardized on vLLM or SGLang for distributed serving.
Where it does not fit
- Single-GPU local deployment of the full checkpoint.
- Organizations without distributed-inference operational capability.
- Teams that equate the 32B active parameter count with 32B-model storage needs.
Open-weight significance
The strategic value of this model is not simply that its weights can be downloaded. The important question is what those weights let an operator control: infrastructure, data location, runtime, adaptation and the ability to exit a provider relationship without discarding the model layer. Those freedoms remain bounded by the model’s license and the practical hardware required to run it.
Frequently asked questions
How large is GLM-4.5?
Z.ai lists 355B total parameters and 32B active parameters.
What is the GLM-4.5 context length?
The published configuration sets max_position_embeddings to 131,072.
What license does GLM-4.5 use?
Z.ai publishes GLM-4.5 under the MIT license and states it can be used commercially and for secondary development.
Which runtimes support GLM-4.5?
The model materials document implementations or use with Transformers, vLLM and SGLang, with additional deployment paths such as Docker Model Runner.
Primary sources and OWM data
Last verified by Open Weight Models: 2026-09-27. Facts can change as model repositories, licenses and runtime support evolve.