@ivanfioravanti: Apple M5 Max + MLX = raw power! Look at this demo I'm playing with "FasterLivePortrait-MLX" I started with MPS but resu…
Summary
The author demonstrates that migrating a LivePortrait implementation from MPS to Apple's MLX framework on an M5 Max chip results in significantly better performance and speed.
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@ivanfioravanti: Interesting video of M5 Max, on impact of Low, Automatic and High power modes on inference. - No external monitor attac…
A performance test demonstrates the impact of Low, Automatic, and High power modes on LLM inference speed on an M5 Max MacBook, showing significant differences in token generation rates and power consumption.
@nash_su: Mac inference speed doubled. MTPLX is an integrated solution combining MLX and MTP, specifically optimized for model inference on Apple Silicon. By using models with a custom MTP head, it can deliver doubled inference speed. I tested it with Qwen3.6-27…
MTPLX is an integrated solution combining MLX and MTP, specifically optimized for model inference speed on Apple Silicon. Tests show that Qwen3.6-27B achieves double the inference speed of LM Studio, and it also integrates fan management.
@neural_avb: I am working on porting SAM models and harness into Apple silicon. Already seeing 1.25x inference speed increase on mlx…
Porting SAM 2.1 models to Apple silicon with MLX, achieving 1.25x inference speed increase on the small model, with quantized versions planned.
I fitted the new δ-mem research for apple silicon using mlx and openclaw integration! My findings
The author implements the δ-mem research paper on Apple Silicon using MLX and OpenClaw, showing memory and attention improvements in local AI agent tests, though with mixed results compared to CUDA benchmarks.
@cevenif: For those running local LLMs on Macs, here's a tool worth watching — Rapid-MLX. It delivers 2-4x faster inference on M-series chips than Ollama, thanks to being built directly on Apple's MLX framework for more thorough utilization of the chip architecture. Key highlights: KV cache pruning plus…
Rapid-MLX is a local LLM inference tool optimized for Apple M-series chips. Built on the MLX framework, it achieves 2 to 4 times faster inference than Ollama, supports multiple models, tool calling, and an OpenAI API-compatible interface.