I spent last weekend comparing two ways to serve a local model: Llamafile and the more traditional vLLM + Docker setup I've been running for months. Same model (Qwen2.5-7B-Instruct), same hardware (a single RTX 4090), same test queries. The gap between them is smaller than I expected, and the trade-offs are worth talking about.
Llamafile is Mozilla's trick — a single executable that bundles the model weights, the inference engine, and a web server into one file. You download it, chmod +x, run it, and there's a chat UI at localhost:8080. No Python environment, no pip install, no Dockerfile. It uses llama.cpp under the hood, so it's CPU-first with GPU offloading where available. The whole thing is about 5 GB for a 7B Q4_K_M quant.
vLLM in Docker is what most of my production pipelines use. You pull the vllm image, mount your model directory, set --tensor-parallel-size 1 and --max-model-len 8192, and you get an OpenAI-compatible /v1/chat/completions endpoint. It's more work upfront — you need Python, CUDA toolkit matching your driver, and a few GB of image layers — but you get PagedAttention, continuous batching, and production-grade throughput.
Here's where it gets interesting.
Latency. For single-user interactive use (chat, quick experiments), Llamafile is faster to first token by about 30-50ms because there's no container networking hop and no Python overhead in the request path. vLLM catches up under load — at 4+ concurrent requests, its continuous batching pulls ahead by 2-3x on total throughput. If you're the only person hitting the endpoint, Llamafile feels snappier. If you're building a service for a team, vLLM wins.
