Phi-4 Local Benchmark: Is Microsoft's Tiny Model Worth Running?

Microsoft's Phi-4 is a 14B parameter model that punches well above its weight class. At 14B parameters, it fits in 9GB VRAM at Q4 quantization — meaning even an RTX 3080 10GB can run it fully on GPU. But does it actually beat bigger models?

Phi-4 Specifications

SpecValue
Parameters14.7B
VRAM at Q4_K_M~9 GB
Context window16K tokens
Training focusReasoning, math, code
LicenseMIT (fully open)

Quick Setup

ollama pull phi4
ollama run phi4

# Or via llama.cpp with GGUF
huggingface-cli download bartowski/phi-4-GGUF   --include "*Q4_K_M*" --local-dir ./models/phi4

./build/bin/llama-server   -m ./models/phi4/phi-4-Q4_K_M.gguf   --n-gpu-layers 999   --ctx-size 16384

Benchmark Results vs Similar Models

ModelMMLUMathHumanEvalVRAM
Phi-4 14B84.880.482.69 GB
Qwen3.6-14B85.278.983.19.5 GB
Mistral-7B64.242.162.55 GB
Gemma 2 9B71.358.271.46 GB
Llama 3.1 8B69.451.972.65.5 GB

Where Phi-4 Excels

Phi-4's training data was heavily curated for mathematical reasoning and step-by-step problem solving. It consistently outperforms models 2-3x its size on:

  • AIME and AMC math competition problems
  • Multi-step logical reasoning
  • Code debugging with explanations
  • Science questions (GSM8K, MATH)

Where It Falls Short

Phi-4 is not a Swiss Army knife. Its 16K context limit hurts for document analysis, and it was not trained for extended creative writing or very long conversations. For general chat at 10B scale, Qwen3.6-14B or Gemma 2 9B are more balanced.

Verdict: Phi-4 is the best math/reasoning model under 10GB VRAM. If your use case involves analysis, coding, or STEM questions, it beats much larger models. See token-speed benchmarks for Phi-4 on different hardware at LLMHardware.io.

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