Budget Pick
AMD Radeon RX 7900 XT20 GB VRAM · ~38 tok/s
Lowest cost that meets recommended VRAM
Check price on AmazonCompatibility Check
Can I Run Gemma 4 31B?
Gemma 4 31B is a 31B parameter model from the Gemma family. Check if your hardware can handle it.
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Social proof
33% of 800 scanned PCs run Gemma 4 31B fully on GPU.
490 keep at least some work on GPU. Based on anonymous compatibility checks.
Full GPU
264Partial GPU
1Hybrid CPU+GPU
225CPU Only
117Can't Run
193Test 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 |
|---|---|---|---|---|---|
| Q3_K_MEasiest | 14.5 GB | 16.5 GB | 20 GB | 20 GB | 8K / 256K |
| Q4_K_M | 18.4 GB | 20.5 GB | 24 GB | 24 GB | 8K / 256K |
| Q8_0 | 33.2 GB | 35 GB | 40 GB | 40 GB | 8K / 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.
Fastest Pick
NVIDIA GeForce RTX 509032 GB VRAM · ~85.2 tok/s
Highest estimated throughput
Check price on AmazonBest Value
NVIDIA GeForce RTX 409024 GB VRAM · ~47.9 tok/s
Best speed per dollar of VRAM
Rent on RunPodNeed 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.