Microsoft · 3.8B · Dense
Lightweight reasoning model
Use Cases
| Quant | Bits | VRAM | Quality | Status |
|---|---|---|---|---|
| Q2_K | 2 | 1.7 GB | low | — |
| Q3_K_M | 3 | 2.2 GB | moderate | — |
| Q4_K_M | 4 | 2.4 GB | good | — |
| Q5_K_M | 5 | 2.9 GB | good | — |
| Q6_K | 6 | 3.4 GB | excellent | — |
| Q8_0 | 8 | 4.4 GB | excellent | — |
| F16 | 16 | 8.3 GB | lossless | — |
GPUs that run Phi-4 Mini Reasoning
Phi-4 Mini Reasoning needs about 2.4 GB of VRAM at Q4_K_M, so it runs comfortably on a GPU with 3 GB of VRAM or more (193 of the GPUs we track). Common cards, smallest first:
| VRAM | GPUs |
|---|---|
| 3 GB | GTX 1060 3GB |
| 4 GB | RX 6500 XT , GTX 1650 SUPER , GTX 1650 Ti |
| 6 GB | RTX 2060 , Arc A380 , RX 5600 XT |
| 8 GB | RTX 5060 Ti , RX 7600 , Arc A750 |
About this model
Phi 4 mini reasoning is designed for multi-step, logic-intensive mathematical problem-solving tasks under memory/compute constrained environments and latency bound scenarios. Some of the use cases include formal proof generation, symbolic computation, advanced word problems, and a wide range of mathematical reasoning scenarios. These models excel at maintaining context across steps, applying structured logic, and delivering accurate, reliable solutions in domains that require deep analytical thinking.
The graph compares the performance of various models on popular math benchmarks for long sentence generation. Phi-4-mini-reasoning outperforms its base model on long sentence generation across each evaluation, as well as larger models like OpenThinker-7B, Llama-3.2-3B-instruct, DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Llama-8B, and Bespoke-Stratos-7B. Phi-4-mini-reasoning is comparable to OpenAI o1-mini across math benchmarks, surpassing the model’s performance during Math-500 and GPQA Diamond evaluations. As seen above, Phi-4-mini-reasoning with 3.8B parameters outperforms models of over twice its size.