Budget Pick
NVIDIA GeForce RTX 3080 10GB10 GB VRAM · ~86.9 tok/s
Lowest cost that meets recommended VRAM
Check price on AmazonCompatibility Check
Can I Run Phi-4 Reasoning 14B?
Phi-4 Reasoning 14B is a 14B parameter model from the Phi family. Check if your hardware can handle it.
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Social proof
61% of 996 scanned PCs run Phi-4 Reasoning 14B fully on GPU.
777 keep at least some work on GPU. Based on anonymous compatibility checks.
Full GPU
611Partial GPU
98Hybrid CPU+GPU
68CPU Only
167Can't Run
52Test 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 | 7 GB | 8 GB | 9.1 GB | 11 GB | 8K / 8K |
| Q5_K_M | 8.8 GB | 10.1 GB | 11.4 GB | 14 GB | 8K / 8K |
| Q8_0 | 14 GB | 16.1 GB | 18.2 GB | 21 GB | 8K / 8K |
| FP16 | 28 GB | 32.2 GB | 36.4 GB | 42 GB | 8K / 8K |
Not sure your GPU has enough VRAM? Compare GPUs that can run Phi-4 Reasoning 14B.
Recommended GPUs for Phi-4 Reasoning 14B
These GPUs meet the recommended 9.1 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 · ~204.8 tok/s
Highest estimated throughput
Check price on AmazonBest Value
NVIDIA GeForce RTX 3080 Ti12 GB VRAM · ~104.2 tok/s
Best speed per dollar of VRAM
Check price on AmazonNeed a detailed comparison? See all GPU rankings for Phi-4 Reasoning 14B.
Strong OpenClaw Model Candidate
Phi-4 Reasoning 14B 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-4 Reasoning 14B?
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.