Budget Pick
NVIDIA GeForce GTX 1650 Super4 GB VRAM · ~66.8 tok/s
Lowest cost that meets recommended VRAM
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Can I Run Phi-3 Mini 3.8B?
Phi-3 Mini 3.8B is a 3.8B parameter model from the Phi family. Check if your hardware can handle it.
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Social proof
78% of 1,596 scanned PCs run Phi-3 Mini 3.8B fully on GPU.
1,263 keep at least some work on GPU. Based on anonymous compatibility checks.
Full GPU
1,248Partial GPU
3Hybrid CPU+GPU
12CPU Only
267Can't Run
66Test 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 | 2.3 GB | 3 GB | 4 GB | 4 GB | 4K / 4K |
| Q8_0 | 4.1 GB | 5 GB | 6 GB | 6 GB | 4K / 4K |
Not sure your GPU has enough VRAM? Compare GPUs that can run Phi-3 Mini 3.8B.
Recommended GPUs for Phi-3 Mini 3.8B
These GPUs meet the recommended 4 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 · ~623.3 tok/s
Highest estimated throughput
Check price on AmazonBest Value
NVIDIA GeForce RTX 3070 Ti8 GB VRAM · ~211.5 tok/s
Best speed per dollar of VRAM
Check price on AmazonNeed a detailed comparison? See all GPU rankings for Phi-3 Mini 3.8B.
Strong OpenClaw Model Candidate
Phi-3 Mini 3.8B 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 Phi-3 Mini 3.8B?
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.