Budget Pick
NVIDIA GeForce RTX 508016 GB VRAM · ~76.8 tok/s
Lowest cost that meets recommended VRAM
Check price on AmazonCompatibility Check
Can I Run GPT-OSS 20B?
GPT-OSS 20B is a 20B parameter model from the GPT-OSS family. Check if your hardware can handle it.
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Social proof
56% of 997 scanned PCs run GPT-OSS 20B fully on GPU.
752 keep at least some work on GPU. Based on anonymous compatibility checks.
Full GPU
562Partial GPU
34Hybrid CPU+GPU
156CPU Only
163Can't Run
82Test Your Hardware
Detecting your hardware...
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 | 10 GB | 11.5 GB | 13 GB | 15 GB | 8K / 8K |
| Q5_K_M | 12.5 GB | 14.4 GB | 16.3 GB | 19 GB | 8K / 8K |
| Q8_0 | 20 GB | 23 GB | 26 GB | 30 GB | 8K / 8K |
| FP16 | 40 GB | 46 GB | 52 GB | 60 GB | 8K / 8K |
Not sure your GPU has enough VRAM? Compare GPUs that can run GPT-OSS 20B.
Recommended GPUs for GPT-OSS 20B
These GPUs meet the recommended 13 GB VRAM for the Q4_K_M quantization. Estimated speeds are approximate and assume full GPU offloading.
Fastest Pick
NVIDIA GeForce RTX 509032 GB VRAM · ~143.4 tok/s
Highest estimated throughput
Check price on AmazonBest Value
NVIDIA GeForce RTX 5070 Ti16 GB VRAM · ~71.7 tok/s
Best speed per dollar of VRAM
Check price on AmazonNeed a detailed comparison? See all GPU rankings for GPT-OSS 20B.
Strong OpenClaw Model Candidate
GPT-OSS 20B 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 20B?
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.