Phi-4 14B is Microsoft's late-2024 release that pushes synthetic-data training to its limit — beats Llama 3.3 70B on MATH and GPQA Diamond despite being 5x smaller. The MIT license is permissive for commercial use. Fits in 16 GB VRAM at Q4_K_M with usable context. Weakness: only a 16K context window, and limited multilingual depth versus Qwen.
Phi-4 14B needs at minimum 8.5 GB of VRAM at Q4_K_M quantization (30.8 GB at FP16). The cheapest GPU that comfortably fits with KV-cache headroom is the Intel Arc A580 12GB (variant) [VERIFY] (12 GB VRAM, $219 MSRP). Community benchmark submissions are open. This model fits a single consumer GPU under 48 GB, so a one-card build works.
Weights only. Add ~20-30% for KV cache at typical context lengths.
| Quant | VRAM | Quality | Notes |
|---|---|---|---|
| FP16 | 30.8 GB | Reference | Training-precision weights |
| Q8_0 | 15.4 GB | Near-lossless | 8-bit, ~0.1% perplexity hit |
| Q6_K | 12.5 GB | Very high | 6-bit, near Q8 quality |
| Q5_K_M | 10.6 GB | High | Strong middle ground |
| Q4_K_M | 8.5 GB | Balanced (recommended) | Default for local deployments |
| Q4_0 | 8.7 GB | Legacy 4-bit | Older GGUF, kept for compatibility |
| Q3_K_M | 6.6 GB | Lossy | When VRAM is very tight |
| Q2_K | 4.9 GB | Extreme | Rescue option, quality degrades visibly |
Filtered from MyAIHardware's GPU database. FP16 needs 30.8 GB; Q4_K_M needs 8.5 GB.
We don't have community benchmarks for this exact model yet.
Official weights + popular runtime tags.
ollama pull phi4lms get bartowski/phi-4-GGUFStep-by-step guides that use this model.
Models in a similar size or capability class.
Use these in papers, blog posts, or internal docs.
@misc{phi-4-14b-2024,
title={Phi-4 14B},
author={Microsoft Research},
year={2024},
url={https://huggingface.co/microsoft/phi-4}
}Microsoft Research (2024). Phi-4 14B [Model card]. Hugging Face. https://huggingface.co/microsoft/phi-4
Phi-4 14B (Phi, Microsoft Research, 2024). Available at https://huggingface.co/microsoft/phi-4.