Phi-4 14B: VRAM Requirements & Local Setup Guide 2026

Microsoft's Phi-4 at 14B parameters. Designed for quality over quantity — strong benchmark performance per parameter. 16K context window.

Provider: Microsoft
Architecture: Dense Transformer
Total Parameters: 14.66B
Active Params/Token: Unknown
Context Window: 16K tokens
License: open
Modalities: text
HuggingFace: microsoft/phi-4

VRAM Requirements by Quantization & Context Length

All values in gigabytes (GB). Calculated using the KV-cache formula with model-specific head fractions. “–” means the context length exceeds this model’s maximum context window. Lower quantization = less VRAM but slightly reduced output quality.

Quantization	4K ctx	8K ctx	32K ctx	128K ctx
Q4_K_M	10.1 GB	10.9 GB	–	–
Q5_K_M	11.5 GB	12.3 GB	–	–
Q6_K	13.0 GB	13.8 GB	–	–
Q8_0	16.3 GB	17.1 GB	–	–
FP16	29.1 GB	29.9 GB	–	–

Weights = 14.66B params × GGUF bits per weight (Q4_K_M ≈ 4.9); plus runtime overhead; plus fp16 KV cache from config.json: 10 KV heads × 128 dims — ~200 KB per token at short context. Same engine as the Can I Run LLM calculator.

Best GPU for Phi-4 14B by Budget

Recommendations assume Q4_K_M quantization at 4K context unless stated otherwise. Higher-end quantizations or longer context windows require more VRAM — consult the table above.

Under $500 (Entry GPU) RTX 3060 12GB (Q4_K_M)
$500–$1,000 (Mid-Range GPU) RTX 4070 12GB
$1,000–$3,000 (High-End GPU) RTX 4090
$3,000+ (Workstation / Cloud) A100

Benchmark Scores

Scores reported by Microsoft or verified third-party evaluations. Higher is better for all benchmarks except where noted.

Benchmark	Score
MMLU-Pro	70.4
HumanEval	82.6
MATH-500	80.4

How to Run Phi-4 14B Locally

Before downloading, verify your GPU has sufficient VRAM using the table above. Insufficient VRAM will cause the model to fall back to CPU offloading, which is significantly slower.

Option A — Ollama (Recommended for Beginners)

Ollama is the easiest way to run Phi-4 14B locally. It handles model downloading, GGUF quantization selection, and serving automatically.

Install Ollama from ollama.com
Pull the model:
```
ollama pull phi4:14b
```
Run interactively:
```
ollama run phi4:14b
```

Option B — HuggingFace Hub

For more control over quantization format, download directly from HuggingFace:

pip install huggingface_hub
huggingface-cli download microsoft/phi-4

Hardware note: Check the VRAM table above before downloading. Running Phi-4 14B requires substantial GPU memory — use Q4_K_M quantization for the lowest VRAM footprint while retaining most model quality.

Strengths and Use Cases

Highly efficient: 70.4% MMLU-Pro and 82.6% HumanEval at just 14B params. Runs on 8–10GB VRAM at Q4_K_M — fits on an RTX 4060 Ti 16GB or RTX 3080.

When choosing a local LLM, Phi-4 14B is worth considering if your workload aligns with its design goals. As a Dense Transformer architecture from Microsoft, it offers a distinct trade-off between compute efficiency and capability. Its dense architecture provides consistent, predictable performance across a wide range of tasks. Whether you are building a local AI pipeline, experimenting with self-hosted chat, or running automated workflows, understanding this model's hardware envelope helps you plan infrastructure realistically.

For deployment, start with the Q4_K_M quantization unless you have headroom for higher precision. Q5_K_M and Q6_K offer improved output quality at the cost of additional VRAM. FP16 is generally only practical on high-VRAM workstation GPUs or cloud instances. Always verify context length requirements before selecting a quantization — longer context windows multiply KV-cache memory consumption significantly, as shown in the VRAM table above.

Ready to Check Your Hardware?

Use our interactive calculator to see exactly whether your GPU can run Phi-4 14B — and at what quantization level.

Check if your GPU can run this model → Compare on LLM Leaderboard

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