Edge
Phi-4 Mini
Small open model family useful for lightweight local prototypes and edge-oriented AI experiments.
Microsoft · Phi
Editorial review
Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.
Best for
Builders testing small local models on laptops, CPUs, and constrained hardware.
Who should use it
- Builders testing small local models on laptops, CPUs, and constrained hardware.
- Builders who want local or self-hosted testing options.
Common workflows
- Small local models, CPU-friendly experiments, edge apps
- edge workflows
- small workflows
- local workflows
Deployment and hardware notes
A strong candidate for CPU or low-VRAM testing with quantized runtimes.
License and usage notes
MIT. Open weights. Verify the exact model card and license terms for the checkpoint or hosted provider you use.
Strengths
- Open weights model option for Phi workflows.
- Builders testing small local models on laptops, CPUs, and constrained hardware.
- A small-model option for local experiments on laptops, CPUs, and low-VRAM machines.
- Tracked as Practical local in the OpenSourcesAI model directory.
Limitations
- Small models are easier to run but need careful prompting and evaluation for complex tasks.
- A strong candidate for CPU or low-VRAM testing with quantized runtimes.
- Context window and limits: 131,072 tokens (RoPE-extended).
- Verify the exact model card, provider docs, license, and serving support before production use.
Local workflow notes
A small-model option for local experiments on laptops, CPUs, and low-VRAM machines.
Local runtimes: Ollama, LM Studio, llama.cpp, Transformers
Platforms: Windows, macOS, Linux, Laptops
Will Phi-4 Mini run on your machine?
Phi-4 Mini is 3.84B parameters and needs 4.1 GB of VRAM at Q4_K_M — 2.6 GB of weights plus 1.5 GB of runtime overhead for the inference server itself.
VRAM by quantization
| Quantization | Weights | Needs (with overhead) | Quality |
|---|---|---|---|
| Q4_K_M | 2.6 GB | 4.1 GB | good |
| Q8_0 | 4.2 GB | 5.7 GB | high |
| FP16 | 7.5 GB | 9 GB | reference |
Fit on common hardware at Q4_K_M
| Hardware | Memory the model can use | System RAM | Verdict |
|---|---|---|---|
| CPU Only | None (CPU only) | 16 GB | CPU offload |
| RTX 4060 Laptop | 8 GB | 16 GB | Comfortable |
| RTX 3060 (12GB) | 12 GB | 32 GB | Comfortable |
| RTX 4060 Ti (16GB) | 16 GB | 32 GB | Comfortable |
Comfortable means VRAM clears the requirement by 2 GB or more. Tight means it covers the requirement with no margin. CPU offload means the model does not fit in VRAM but system RAM is at least 1.6× the weights, so it will run at reduced speed — expect roughly 1–5 tokens per second. Figures are weights plus a fixed runtime overhead and exclude KV-cache growth, which scales with context length.
Need more hardware for Phi-4 Mini? Open the PC Builder for the 7B / 8B tier →
VRAM fit by quantization level
Enter your GPU VRAM below to see which quantization of Phi-4 Mini fits and get the Ollama run command.
Sources to verify
Related resources
Continue with model source notes, local tools, and implementation guides related to this model.
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