Agents
GLM-4.5
Earlier GLM open-weight MoE model useful as a baseline for agent, coding, and reasoning workflows.
Z.ai · GLM
Editorial review
Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.
Best for
Teams comparing prior GLM releases with current GLM-5.1 behavior.
Who should use it
- Teams comparing prior GLM releases with current GLM-5.1 behavior.
- Teams with access to hosted inference or server-class deployment paths.
- Developers evaluating coding assistant, repo-editing, and code review workflows.
- Teams testing tool-use, agentic planning, and multi-step workflow behavior.
Common workflows
- Agentic apps, coding, tool use, reasoning
- agents workflows
- coding workflows
- reasoning workflows
- legacy workflows
Deployment and hardware notes
Full model is server-class; Air or quantized builds are more practical.
License and usage notes
Check model card. Open weights. Verify the exact model card and license terms for the checkpoint or hosted provider you use.
Strengths
- Open weights model option for GLM workflows.
- Teams comparing prior GLM releases with current GLM-5.1 behavior.
- Tracked as Legacy baseline in the OpenSourcesAI model directory.
Limitations
- Demote behind GLM-5.1 for current 2026 coverage; production fit depends on serving support and license review.
- Full model is server-class; Air or quantized builds are more practical.
- Context window and limits: 128k noted in Z.ai docs.
- Verify the exact model card, provider docs, license, and serving support before production use.
Will GLM-4.5 run on your machine?
GLM-4.5 is 355B parameters and needs 217.5 GB of VRAM at Q4_K_M — 216 GB of weights plus 1.5 GB of runtime overhead for the inference server itself.
VRAM by quantization
| Quantization | Weights | Needs (with overhead) | Quality |
|---|---|---|---|
| Q4_K_M | 216 GB | 217.5 GB | good |
| Q8_0 | 381 GB | 382.5 GB | high |
Fit on common hardware at Q4_K_M
| Hardware | Memory the model can use | System RAM | Verdict |
|---|---|---|---|
| CPU Only | None (CPU only) | 16 GB | Too large |
| RTX 4060 Laptop | 8 GB | 16 GB | Too large |
| RTX 3060 (12GB) | 12 GB | 32 GB | Too large |
| RTX 4060 Ti (16GB) | 16 GB | 32 GB | Too large |
| RTX 3090 | 24 GB | 64 GB | Too large |
| Apple Silicon (Unified Memory) 36 GB | 27 GB of 36 GB | 36 GB | Too large |
| RTX 5090 | 32 GB | 64 GB | Too large |
| Apple Silicon (Unified Memory) 48 GB | 36 GB of 48 GB | 48 GB | Too large |
| Apple Silicon (Unified Memory) 64 GB | 48 GB of 64 GB | 64 GB | Too large |
| Apple Silicon (Unified Memory) 96 GB | 72 GB of 96 GB | 96 GB | Too large |
| Apple Silicon (Unified Memory) 128 GB | 96 GB of 128 GB | 128 GB | Too large |
| Apple Silicon (Unified Memory) 192 GB | 144 GB of 192 GB | 192 GB | Too large |
Comfortable means VRAM clears the requirement by 2 GB or more. Tight means it covers the requirement with no margin. CPU offload means the model does not fit in VRAM but system RAM is at least 1.6× the weights, so it will run at reduced speed — expect roughly 1–5 tokens per second. Figures are weights plus a fixed runtime overhead and exclude KV-cache growth, which scales with context length.
Apple Silicon shares one pool of memory between the system and the GPU, so a model cannot use all of it. These rows apply the same 75% usable fraction the Compatibility Checker uses, which is why a 36 GB Mac is graded on less than 36 GB.
VRAM fit by quantization level
Enter your GPU VRAM below to see which quantization of GLM-4.5 fits and get the Ollama run command.
Sources to verify
Related resources
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Model ecosystem connections
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Setup and deployment
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