Autor: Jakub Rusinowski · Ostatnia aktualizacja: 10 lipca 2026
Microsoft's latest model focusing on high-quality synthetic data training. Phi-4 14B outperforms many models twice its size in reasoning benchmarks.
| Phi-4 (14B) | Min 9 GB VRAM · Q4_K_M · 16,000 ctx · ollama run phi4 |
The cheapest GPU that runs Phi-4 Family locally (min 9 GB VRAM) is the Intel Arc B570 (10 GB).
Install Ollama then run: ollama run phi4
Minimum VRAM: 9 GB. For best results use Q4_K_M quantization.
Pick a quantization and open it in LM Studio, Ollama, or Jan, or download the raw .gguf file directly. Quant list and sizes resolved from Hugging Face.
| Quant | Size | Download (.gguf) |
|---|---|---|
| Q3_K_M | 5.97 GB (est.) | phi-4-Q3_K_M.gguf |
| Q4_K_M | 8.45 GB (est.) | phi-4-Q4_K_M.gguf |
| Q5_K_M | 9.92 GB (est.) | phi-4-Q5_K_M.gguf |
| Q6_K | 11.48 GB (est.) | phi-4-Q6_K.gguf |
| Q8_0 | 14.88 GB (est.) | phi-4-Q8_0.gguf |
Download in LM Studio: lms get bartowski/phi-4-GGUF
Want this model on your phone? You can run it on your desktop with LM Studio and chat from your iPhone or iPad over an encrypted link — see Run LM Studio Models on Your Phone (LM Link).
Phi-4 Family needs about 9 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Phi-4 (14B) (9 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — Phi-4 Family runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
Q4_K_M is the best balance of quality and VRAM for Phi-4 Family in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
Install Ollama, then run: ollama run phi4. This downloads Phi-4 Family and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.