Phi-4 14B
Microsoft's Phi-4 14B, trained on ultra-curated synthetic data with a heavy STEM bias. The 14B reasoning leader at the end of 2024.
By Microsoft · United States
Updated 2026-09-15
When to pick this model
- Math, science, and structured reasoning workloads
- Coding assistants where quality beats context length
- MIT-licensed commercial deployments
- Mid-size GPU deployments needing strong reasoning
- Replacing larger models on STEM-heavy benchmarks
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 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 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 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 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 hardware should you buy to run Phi-4 14B?
To run Phi-4 14B locally at Q4, you need ~9 GB for Q4 weights alone. Hardware option to compare: RTX 5070 12GB (ASUS Prime OC). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.
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Published benchmark scores
| Benchmark | Score |
|---|---|
| MMLU | 84.8 |
| MATH | 80.4 |
| HumanEval | 82.6 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Phi-4 14B in context: its MMLU score of 84.8 ranks #6 of the 34 catalog models with a published MMLU result (catalog median 73.4); its HumanEval score of 82.6 ranks #12 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- Top-tier 14B reasoning at release
- MIT license
- Strong math, science, and code performance
- Tight, well-formatted outputs
Limitations
- 16k context is a significant limitation
- Weaker multilingual coverage than Qwen
- Narrower world knowledge from synthetic training
Typical workloads
In our catalog grid, Phi-4 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.
Note the 16k-token context window — fine for short interactions, limiting for long documents or big retrieval contexts. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense · 14B · Phi-4 · Microsoft-exclusive synthetic data
Training: Ultra-filtered Microsoft synthetic corpus. Focus on reasoning and math.
The reasoning-focused 14B to pick — just budget around its short context window.
Quick start
Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.
Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.
ollama run phi4:14bOr 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 14B need?
At the recommended Q4_K_M quantization, Phi-4 14B needs about 9 GB of VRAM. Q8_0 takes 16 GB, and unquantized FP16 weights take 28 GB.
Can Phi-4 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 14B support?
Phi-4 14B supports a 16k-token context window (16,384 tokens).
Can I use Phi-4 14B commercially?
Yes. Phi-4 14B is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Phi-4 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 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.