Phi-4 Mini
Phi · MIT
Microsoft's tiny powerhouse. Punches well above its weight at 3.8B parameters, competitive with many 7B models on reasoning and coding benchmarks. Extremely fast inference. Ideal as a draft model or for resource-constrained setups.
- Parameters
- 3.82B
- Architecture
- Dense
- Context
- 16,384 tokens
- Released
- 2025-02-27
- Engines
- llama.cpp, ollama, vLLM
- Builder Tools
- Cursor, Continue, Ollama, LM Studio
Parameters
3.82B
VRAM
3.3 GB
Context
16K
Formats
5
GPUs
43
Phi-4 Mini (3.82B) requires 3.3 GB VRAM at recommended quality (Q6_K). At efficient quality (Q4_K_M), it fits in 2.4 GB VRAM, making it compatible with the NVIDIA RTX 4060 Laptop (40-60W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 580 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.
Source: OwnRig methodology
3.3 GB
Q6_K
2.9 GB
16K tokens
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VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 4.3 GB | 3.8 GB |
| recommended | Q6_K | 3.3 GB | 2.9 GB |
| recommended | Q5_K_M | 2.8 GB | 2.5 GB |
| efficient | Q4_K_M | 2.4 GB | 2.1 GB |
| compressed | Q3_K_M | 2 GB | 1.7 GB |
Context Length Impact
KV cache VRAM at Q6_K quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 102 MB | 3.4 GB |
| 4K | 102 MB | 3.4 GB |
| 8K | 307 MB | 3.6 GB |
| 16K | 512 MB | 3.8 GB |
Compatible GPUs
43 devicesShowing 43 of 43 entries
Builder Context
Phi-4 Mini is commonly used with Cursor, Continue, Ollama, LM Studio. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.
Recommended Builds
Complete PC builds that can run Phi-4 Mini.
Frequently Asked Questions
- How much VRAM does Phi-4 Mini need?
- Phi-4 Mini requires 3.3 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 2 GB.
- What is the best GPU for Phi-4 Mini?
- The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Phi-4 Mini, achieving 580 tok/s at Q8_0 with an excellent rating.
- Can I run Phi-4 Mini on an RTX 4060 Ti?
- Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Phi-4 Mini runs at 68 tok/s (Q8_0, excellent).
- What quantization should I use for Phi-4 Mini?
- For the best quality, use Q6_K (3.3 GB VRAM). If your GPU has limited VRAM, Q3_K_M (2 GB) is the most efficient option with acceptable quality.
- Is Phi-4 Mini good for coding?
- Yes. Phi-4 Mini is used with Cursor, Continue, Ollama, LM Studio for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.
Related Guides
Data confidence: estimated. Source
VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.Phi is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.