Running Gemma 2 27B Locally: MLX vs vLLM vs llama.cpp Performance Comparison

You run Gemma 2 27B on MLX the day it drops, feed it some multimodal prompts, and get nonsense hallucinations. Meanwhile, Reddit threads are full of people saying it's the best 27B model yet. Something doesn't add up. The problem isn't the model — it's the inference harness. Each framework makes different tradeoffs in quantization, attention implementation, and memory layout. Run the same model on MLX, vLLM, and llama.cpp, and you'll get three different experiences. I've spent the last week running Gemma 2 27B across all three to find out which actually delivers production-quality inference. Why Your MLX Results Look Wrong MLX optimizes for Apple Silicon's unified memory architecture, but Gemma 2's architecture fights it. The model uses sliding window attention with local and global attention heads — a pattern that doesn't map cleanly to MLX's matrix operations. When you quantize to 4-bit with MLX's default quantization scheme, those attention patterns degrade fast. Here's what most pe