Apple Silicon Inference Optimization Is a Mess: vllm-metal Closest to Complete Stack, mlx-lm Lacks Key Features
McFlurriez · reddit · 2026-08-16
After two weeks of research, the author finds the Apple Silicon inference optimization ecosystem fragmented, lacking integrated optimizations like CUDA. Key issues: Qwen's hybrid KV/recurrent state complicates prefix caching and speculative decoding; mlx-lm drops MTP heads during conversion. vllm-metal is closest to a complete stack; the author urges consolidating efforts.
More from Infra
- 14 open-source LLM observability tools to know in 2026, from Langfuse to Laminar — TheTuringPost · 2026-10-16
- Local AI is a routing problem, not a privacy feature: where each task should run — bigdata · 2026-10-16
- Moving agent work from GPUs to CPUs for durable, predictable actions — cramforce · 2026-10-16
- First TPUv7 agentic benchmark on vLLM claims 1.42x perf-per-dollar over GB300 NVL72 — vllm_project · 2026-10-16
- Phison CEO calls CXMT memory scare a 'ghost story', warns of materials shortage — BenBajarin · 2026-10-16
- Ben Bajarin: The Future of Compute Is Fungible as Hyperscalers Design for Workload Flexibility — BenBajarin · 2026-10-16