ChatCodingReasoningMulti-Purpose14B
Phi-3 Medium 14B Instruct
Phi · MIT
14B model with strong reasoning and coding performance. Fits comfortably on 16GB GPUs at Q4 and excels at structured output tasks. MIT license makes it attractive for commercial use.
- Parameters
- 14B
- Context
- 128,000 tokens
- Released
- 2024-05-21
- Engines
- llama.cpp, ollama, ONNX Runtime
Phi-3 Medium 14B Instruct (14B) requires 9.7 GB VRAM at recommended quality (Q5_K_M). At efficient quality (Q4_K_M), it fits in 8.2 GB VRAM, making it compatible with the NVIDIA GeForce RTX 4060 8GB. On NVIDIA GeForce RTX 4090, expect approximately 55 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.
— OwnRig methodology, data updated 2026-03-01
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 15.2 GB | 14 GB |
| recommended | Q5_K_M | 9.7 GB | 8.4 GB |
| efficient | Q4_K_M | 8.2 GB | 7 GB |
| compressed | Q3_K_M | 6.7 GB | 5.5 GB |
Context Length Impact
KV cache VRAM at Q5_K_M quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 205 MB | 9.9 GB |
| 4K | 410 MB | 10.1 GB |
| 8K | 819 MB | 10.5 GB |
| 16K | 1.5 GB | 11.2 GB |
| 32K | 3.1 GB | 12.8 GB |
| 64K | 6.1 GB | 15.8 GB |
Compatible GPUs
Performance data for Phi-3 Medium 14B Instruct across different hardware.
| Device | Quantization | Speed | Rating | Fits in VRAM |
|---|---|---|---|---|
| NVIDIA GeForce RTX 4060 Ti 16GB | Q5_K_M | 28 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 4090 | Q8_0 | 55 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4060 8GB | Q3_K_M | 20 tok/s | Marginal | ✓ |
| NVIDIA GeForce RTX 4070 Ti 12GB | Q3_K_M | 35 tok/s | Acceptable | ✓ |
| NVIDIA GeForce RTX 3080 10GB | Q3_K_M | 32 tok/s | Acceptable | ✓ |
| Apple M3 Pro (18GB Unified) | Q3_K_M | 6 tok/s | Marginal | ✓ |
Recommended Builds
Complete PC builds that can run Phi-3 Medium 14B Instruct.
Frequently Asked Questions
- How much VRAM does Phi-3 Medium 14B Instruct need?
- Phi-3 Medium 14B Instruct requires 9.7 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 6.7 GB.
- What is the best GPU for Phi-3 Medium 14B Instruct?
- The NVIDIA GeForce RTX 4090 delivers the best performance for Phi-3 Medium 14B Instruct, achieving 55 tok/s at Q8_0 with an excellent rating.
- Can I run Phi-3 Medium 14B Instruct on an RTX 4060 Ti?
- Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Phi-3 Medium 14B Instruct runs at 28 tok/s (Q5_K_M, good).
- What quantization should I use for Phi-3 Medium 14B Instruct?
- For the best quality, use Q5_K_M (9.7 GB VRAM). If your GPU has limited VRAM, Q3_K_M (6.7 GB) is the most efficient option with acceptable quality.
- Is Phi-3 Medium 14B Instruct good for coding?
- Phi-3 Medium 14B Instruct supports coding use cases. For the best coding experience, pair it with an embedding model for local RAG.
Data confidence: verified. Last updated: 2026-03-01. Source