Budget Pick
Apple M4 Ultra256 GB VRAM · ~4.9 tok/s
Lowest cost that meets recommended VRAM
Check price on AmazonCompatibility Check
Can I Run GLM 4.7?
GLM 4.7 is a 355B parameter model from the GLM family. Check if your hardware can handle it.
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Social proof
1% of 999 scanned PCs run GLM 4.7 fully on GPU.
211 keep at least some work on GPU. Based on anonymous compatibility checks.
Full GPU
13Hybrid CPU+GPU
198CPU Only
88Can't Run
700Test Your Hardware
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Hardware Requirements
Beginner tip: minimum values mean the model can start, while recommended values usually feel smoother during real use. VRAM is your GPU's dedicated memory; RAM is your system memory used as fallback. See the full glossary.
| Quantization | File Size | Min VRAM | Recommended VRAM | Min RAM | Context |
|---|---|---|---|---|---|
| Q4_K_MEasiest | 177.5 GB | 204.1 GB | 230.8 GB | 267 GB | 8K / 8K |
| Q5_K_M | 221.9 GB | 255.2 GB | 288.5 GB | 333 GB | 8K / 8K |
| Q8_0 | 355 GB | 408.2 GB | 461.5 GB | 533 GB | 8K / 8K |
| FP16 | 710 GB | 816.5 GB | 923 GB | 1065 GB | 8K / 8K |
Not sure your GPU has enough VRAM? Compare GPUs that can run GLM 4.7.
Recommended GPUs for GLM 4.7
These GPUs meet the recommended 230.8 GB VRAM for the Q4_K_M quantization. Estimated speeds are approximate and assume full GPU offloading.
Need a detailed comparison? See all GPU rankings for GLM 4.7.
Strong OpenClaw Model Candidate
GLM 4.7 is a common OpenClaw pick for local agent workflows. Use this model with Ollama, llama.cpp, or LM Studio, then confirm full OpenClaw hardware compatibility.
Why choose GLM 4.7?
General-purpose local model brief
- • Pilot testing with your own tasks
- • Controlled local experiments
Quantization tip: Benchmark at least two quantizations and validate with a task-specific eval set before production use.