Microsoft
Phi 4 Mini 4B (4B parameters) requires approximately 5.7 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 7 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run Phi 4 Mini 4B on your machine.
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ollama run phi4-miniQuick specs
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No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | — |
Q3_K_S | 3 | 2.0 GB | Low | — |
NVFP4 | 4 | 2.2 GB | Medium | — |
Q4_K_M | 4 | 2.4 GB | Medium | — |
Q5_K_M | 5 | 2.9 GB | High | — |
Q6_K | 6 | 3.3 GB | High | — |
Q8_0 | 8 | 4.3 GB | Very High | — |
F16 | 16 | 8.2 GB | Maximum | — |
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General
Source: official · 2025-02-27
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Phi 4 Mini 4B (4B parameters) requires approximately 5.7 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc A380 6GB can run Phi 4 Mini 4B with a compatibility score of 72/100. It provides 6 GB of memory and achieves approximately 40.2 tokens per second.
The recommended quantization for Phi 4 Mini 4B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for Phi 4 Mini 4B: RTX 5060 8GB (score: 77/100), RTX 5060 Ti 8GB (score: 77/100), RTX 5050 8GB (score: 77/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Phi 4 Mini 4B is well-suited for chat as well as reasoning. It was designed with these use cases in mind.
See also
