Fragmentation in Apple Silicon Inference Optimization Stacks

Current Apple Silicon inference frameworks lack a unified stack comparable to NVIDIA/CUDA, with critical optimizations like prefix caching, speculative decoding, and continuous batching scattered across fragmented tools such as mlx-lm and vllm-metal. This fragmentation prevents consistent performance for latest Qwen models compared to mature CUDA implementations.