Budget Pick
Apple M2 Max96 GB VRAM · ~5.3 tok/s
Lowest cost that meets recommended VRAM
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Can I Run GPT-OSS 120B?
GPT-OSS 120B is a 120B parameter model from the GPT-OSS family. Check if your hardware can handle it.
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Social proof
6% of 997 scanned PCs run GPT-OSS 120B fully on GPU.
291 keep at least some work on GPU. Based on anonymous compatibility checks.
Full GPU
59Hybrid CPU+GPU
232CPU Only
99Can't Run
607Test 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 | 60 GB | 69 GB | 78 GB | 90 GB | 8K / 8K |
| Q5_K_M | 75 GB | 86.3 GB | 97.5 GB | 113 GB | 8K / 8K |
| Q8_0 | 120 GB | 138 GB | 156 GB | 180 GB | 8K / 8K |
| FP16 | 240 GB | 276 GB | 312 GB | 360 GB | 8K / 8K |
Not sure your GPU has enough VRAM? Compare GPUs that can run GPT-OSS 120B.
Recommended GPUs for GPT-OSS 120B
These GPUs meet the recommended 78 GB VRAM for the Q4_K_M quantization. Estimated speeds are approximate and assume full GPU offloading.
Fastest Pick
Apple M4 Ultra256 GB VRAM · ~14.6 tok/s
Highest estimated throughput
Check price on AmazonBest Value
Apple M1 Ultra128 GB VRAM · ~10.7 tok/s
Best speed per dollar of VRAM
Check price on AmazonNeed a detailed comparison? See all GPU rankings for GPT-OSS 120B.
Strong OpenClaw Model Candidate
GPT-OSS 120B 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 GPT-OSS 120B?
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.