Phi-3 vs LLaMA 3 8B: Small Model Showdown

Phi-3 vs LLaMA 3 8B: The Small-Model Tier

Microsoft’s Phi-3 Mini (3.8B) punches well above its weight class, routinely matching models twice its size on reasoning benchmarks. Meta’s LLaMA 3 8B remains the default choice for many teams deploying on dedicated GPU servers. This comparison helps you decide which small model deserves your GPU time and budget.

Both models are excellent candidates for latency-sensitive applications where every millisecond counts. For full hosting details, visit our Phi hosting and LLaMA hosting pages.

Specifications Side by Side

Phi-3’s standout feature is its 128K context window at less than half the parameter count. It achieves this through aggressive data curation and a curriculum-based training approach that emphasises reasoning and quality over raw scale. LLaMA 3 8B counters with 15 trillion training tokens and broader general knowledge.

Quality and Speed Benchmarks

Tested on an RTX 4060 (8 GB VRAM) with Ollama. See our tokens-per-second benchmark tool for live data.

Phi-3 Mini at full precision outperforms quantised LLaMA 3 8B on reasoning (GSM8K) and general knowledge (MMLU). LLaMA 3 leads on code generation (HumanEval). For throughput, Phi-3’s smaller size translates directly to faster inference on memory-constrained cards. Visit the benchmarks hub for more data points.

VRAM Footprint

Phi-3 Mini fits at FP16 on an 8 GB GPU, making it one of the few models that runs unquantised on budget hardware. LLaMA 3 8B needs quantisation to fit on the same card but runs at FP16 on a 24 GB RTX 3090. See our LLaMA 3 VRAM requirements guide for full sizing tables.

Deployment Options

# Phi-3 Mini via Ollama
ollama run phi3:mini

# LLaMA 3 8B via vLLM (RTX 3090)
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Meta-Llama-3-8B-Instruct \
  --dtype float16 --max-model-len 8192

Both models work seamlessly with Ollama and vLLM. Our vLLM vs Ollama guide covers framework trade-offs in detail. Use the cost-per-million-tokens calculator to compare operating costs.

Which to Choose

Pick Phi-3 Mini for edge-style deployments, budget GPUs, reasoning-focused workloads, and scenarios where MIT licensing is required. Its 128K context window is a standout advantage. Also see our Run Phi-3 on a Dedicated Server guide.

Pick LLaMA 3 8B for broader general knowledge, better code generation, and access to the largest open-model ecosystem including fine-tuned variants. See the best GPU for LLM inference guide for hardware pairing advice.