Reasoning
Phi-4
Phi-4 is a Phi-family model useful for small language model, edge, and low-resource workflow evaluation.
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
Small language model reasoning and assistant workflows
Who should use it
- Small language model reasoning and assistant workflows
- Builders who want local or self-hosted testing options.
Common workflows
- Small language model reasoning and assistant workflows
- small workflows
- edge workflows
- local workflows
- efficient workflows
Deployment and hardware notes
Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
License and usage notes
Check exact model card. Open weights where released. Verify the exact model card and license terms for the checkpoint or hosted provider you use.
Strengths
- Open weights where released model option for Phi workflows.
- Small language model reasoning and assistant workflows
- Use the exact checkpoint and quantization that matches your hardware and latency target.
Limitations
- Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
- Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
- Context window and limits: 16,384 tokens.
- Verify the exact model card, provider docs, license, and serving support before production use.
Local workflow notes
Use the exact checkpoint and quantization that matches your hardware and latency target.
Local runtimes: Ollama where supported, LM Studio where supported, llama.cpp where supported, Transformers
Platforms: Windows, macOS, Linux
Will Phi-4 run on your machine?
Phi-4 is 14B parameters and needs 10.6 GB of VRAM at Q4_K_M — 9.1 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 | 9.1 GB | 10.6 GB | good |
| Q8_0 | 15 GB | 16.5 GB | high |
| FP16 | 28 GB | 29.5 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 | CPU offload |
| RTX 3060 (12GB) | 12 GB | 32 GB | Tight |
| RTX 4060 Ti (16GB) | 16 GB | 32 GB | Comfortable |
| RTX 3090 | 24 GB | 64 GB | Comfortable |
| Apple Silicon (Unified Memory) 36 GB | 27 GB of 36 GB | 36 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.
Apple Silicon shares one pool of memory between the system and the GPU, so a model cannot use all of it. These rows apply the same 75% usable fraction the Compatibility Checker uses, which is why a 36 GB Mac is graded on less than 36 GB.
VRAM fit by quantization level
Enter your GPU VRAM below to see which quantization of Phi-4 fits and get the Ollama run command.
Related resources
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