AI & Machine Learningaeyeops/aeo-skill-marketp...
mlx-serving
Use when the user asks about MLX serving, mlx_lm.server, oMLX, Apple Silicon LLM serving, or
running a local LLM on a Mac, and when troubleshooting a model that fails to load, OOM during
load or inference, a server that hangs or crashes at batch>1, tool calls returned as plaintext
content, a throughput regression, or the choice between mlx-lm and oMLX. Also covers oMLX
feature-flag tuning (turboquant_kv, dflash, MTP, specprefill, thinking_budget,
max-concurrent-requests, force_sampling), the OptiQ proxy for models exceeding RAM, Llama-4
ChunkedKVCache batch handling, Llama-3 tool-call JSON format, and bench-driven validation of
serving configs. Apple Silicon M-series only, not cloud LLM hosting, non-MLX backends such as
llama.cpp, Ollama and vLLM, or model training.