Phi-4 14B
Microsoft's Phi-4 14B trained on synthetic textbook-quality data. Punches above weight on reasoning and math; MIT licensed.
Phi-4 14B is the strongest entry in the Phi line and a legitimate alternative to Qwen 2.5 14B / Qwen 3 14B in the 12–16 GB VRAM bracket. It earns the score by being unusually strong on math and reasoning relative to its parameter count — the Phi philosophy paying off.
Strengths- Math + structured reasoning lead the size class — beats Qwen 2.5 14B on GSM8K and MATH.
- MIT license — cleanest license in the 14B tier.
- Knowledge curation shows — fewer hallucinations on technical content.
- Open-domain knowledge is shallower than Qwen / Llama at similar size — synthetic textbook training has tradeoffs.
- Refusal behavior is conservative — over-cautious on dual-use technical questions.
- Multilingual is weak — English-first training shows.
- Q4_K_M (8.4 GB): 70–85 tok/s decode, TTFT ~100 ms
- Q5_K_M (9.9 GB): 60–75 tok/s
- Q8_0 (14.7 GB): 42–52 tok/s
Yes, for math and reasoning workloads, technical writing, code review tasks. Strongest 14B for those jobs. No, for general open-domain chat, multilingual workloads, or anything requiring broad pop-culture / current-events knowledge.
How it compares- vs Phi-3.5 Mini (3.8B) → Phi-4 is materially more capable across the board; different VRAM tier.
- vs Phi-4 Reasoning 14B → Reasoning variant pushes hard problems further with chain-of-thought; base Phi-4 is faster on simple prompts.
- vs Qwen 2.5 14B → Phi-4 wins on math/reasoning; Qwen wins on knowledge breadth and multilingual.
- vs Qwen 3 14B → coin flip on hard tasks. Qwen 3 has hybrid mode flexibility; Phi-4 has cleaner license.
ollama pull phi4:14b-q4_K_M
ollama run phi4:14b-q4_K_M
›Why this rating
8.6/10 — Microsoft's curated-data approach scaled to 14B. Reasoning quality is genuinely impressive — competitive with much larger models — and the synthetic-textbook training shows on math and structured tasks. Loses points only because Qwen 3 14B's hybrid mode offers more flexibility.
Overview
Microsoft's Phi-4 14B trained on synthetic textbook-quality data. Punches above weight on reasoning and math; MIT licensed.
Strengths
- MIT license
- Strong math and reasoning per param
- 16K context
Weaknesses
- Smaller context than Qwen/Llama
- Synthetic-data training shows in creative tasks
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 8.4 GB | 11 GB |
| Q8_0 | 15.0 GB | 18 GB |
Get the model
Ollama
One-line install
ollama run phi4:14bRead our Ollama review →HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Phi-4 14B.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Phi-4 14B?
Can I use Phi-4 14B commercially?
What's the context length of Phi-4 14B?
How do I install Phi-4 14B with Ollama?
Source: huggingface.co/microsoft/phi-4
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.