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33% of 809 scanned PCs run Gemma 4 31B fully on GPU.

495 keep at least some work on GPU. Based on anonymous compatibility checks.

Full GPU
269
Partial GPU
1
Hybrid CPU+GPU
225
CPU Only
121
Can't Run
193

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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.

QuantizationFile SizeMin VRAMRecommended VRAMMin RAMContext
Q3_K_MEasiest14.5 GB16.5 GB20 GB20 GB8K / 256K
Q4_K_M18.4 GB20.5 GB24 GB24 GB8K / 256K
Q8_033.2 GB35 GB40 GB40 GB8K / 256K

Not sure your GPU has enough VRAM? Compare GPUs that can run Gemma 4 31B.

Recommended GPUs for Gemma 4 31B

These GPUs meet the recommended 20 GB VRAM for the Q3_K_M quantization. Estimated speeds are approximate and assume full GPU offloading.

Need a detailed comparison? See all GPU rankings for Gemma 4 31B.

Strong OpenClaw Model Candidate

Gemma 4 31B 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 Gemma 4 31B?

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.

Full Model DetailsBest GPU for Gemma 4 31BCheck on RTX 4090Gemma 4 31B pros & consSetup GuidesDecision WizardBrowse All Models