mlx-optimizer-plugin

Optimize Python MLX inference and generation loops with warmup, batching, cache handling, synchronization, quantization, and memory checks.

Guide MLX Metal profiling, mx.fast escalation, custom Metal kernels, and C++ extensions when profiling proves kernel-level bottlenecks.

Route Python-first MLX optimization work on Apple Silicon to focused audit, training, inference, Metal, or bridge workflows.

Audit Python MLX repos for lazy-eval, synchronization, compile, dtype, memory, progress, and benchmark issues.

Advise on Python-first MLX integration with Swift, C, C++, and non-native language boundaries.

Optimize Python MLX training loops with value_and_grad, accumulation, checkpointing, dtype, validation cadence, memory telemetry, and progress reporting.