Phi-4 Reasoning 14B
By Microsoft · United States
Updated 2026-07-13
Overview
Microsoft's 14B reasoner that beats R1-Distill-Llama-70B on AIME and GPQA with 50x fewer parameters. MIT-licensed, English-first, with a 32K context.
When to pick this model
- You want frontier-class reasoning that fits on a 16GB or 24GB GPU
- You need MIT licensing for commercial deployment
- You're solving math, science, or logic problems in English
- You want to replace a 70B reasoner with something far cheaper to run
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 9 GB |
| Q5_K_M | 11 GB |
| Q8_0 | 16 GB |
| FP16 (no quantization) | 28 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Phi-4 Reasoning 14B needs a 12 GB card at Q4_K_M (9 GB). Stepping up to Q8_0 nearly doubles the footprint to 16 GB, and unquantized FP16 weights take 28 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Phi-4 Reasoning 14B needs roughly 16 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 6 tokens/sec on entry-level GPUs, on the order of 20 tokens/sec on a mid-range card, and up to 55 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Phi-4 Reasoning 14B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Phi-4 Reasoning 14B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 9 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q5_K_M (11 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (16 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q8_0 (16 GB used) |
| 32 GB | RTX 5090 | FP16 (28 GB used) |
Which GPU should you buy to run Phi-4 Reasoning 14B?
To run Phi-4 Reasoning 14B locally at Q4, you need ~9 GB of VRAM. The best value for this is a RTX 5070 (12 GB VRAM).
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Strengths
- Beats R1-Distill-Llama-70B on AIME and GPQA with 50x fewer parameters
- MIT license
- Increased RoPE base frequency improves long-form reasoning
- Practical hardware footprint for a frontier-class reasoner
Limitations
- English-first — weak multilingual performance
- Weaker on non-Python code generation
- 32K context vs 128K on most peers
Typical workloads
In our catalog grid, Phi-4 Reasoning 14B is filed under Math, Logic, Studies — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks.
The 32k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense · SFT on o3-mini traces · Plus variant adds RL
Training: RoPE base freq. increased vs Phi-4 base.
The most efficient open reasoner you can run on a single consumer GPU.
Quick start
ollama run phi4-reasoning:14bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Phi-4 Reasoning 14B need?
At the recommended Q4_K_M quantization, Phi-4 Reasoning 14B needs about 9 GB of VRAM. Q8_0 takes 16 GB, and unquantized FP16 weights take 28 GB.
Can Phi-4 Reasoning 14B run without a GPU?
Yes — with roughly 16 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Phi-4 Reasoning 14B support?
Phi-4 Reasoning 14B supports a 32k-token context window (32,768 tokens).
Can I use Phi-4 Reasoning 14B commercially?
Yes. Phi-4 Reasoning 14B is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Phi-4 Reasoning 14B on consumer hardware?
Our compatibility engine estimates on the order of 20 tokens/sec on a mid-range GPU and up to 55 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Phi-4 Reasoning 14B should I download first?
Start with Q4_K_M (9 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q8_0.