MLX crushes GGUF in Qwen 3.5 M4 Max benchmarks

For Mac users, MLX is becoming the undisputed performance king for large-scale local LLMs, but GGUF still holds a critical feature advantage in multi-turn stability.

• –MLX achieved 34.7 t/s versus GGUF's 15.8 t/s in a massive 80k context window test.
• –Memory usage for the 6-bit MLX quant was ~5GB lower than the 5-bit GGUF, highlighting superior optimization on Apple Silicon.
• –Despite the raw performance lead, community members note that GGUF (via llama.cpp) still provides more reliable prompt caching and better integration with agentic toolchains.
• –The 122B-A10B's sparse Mixture-of-Experts architecture scales remarkably well on unified memory, but choice of quantization remains the primary bottleneck for inference latency.