AI Briefing#mlx
The latest AI and developer news about #mlx, with the original source and a short summary.
The latest AI and developer news about #mlx, with the original source and a short summary.
A lightweight, Mac-exclusive coding agent built on Apple's MLX framework has been released.
MTPLX, an engine that leverages MTP heads to boost LLM inference speed by up to 2.24x on Apple Silicon, has been released.
On a Mac Mini M4 Pro 64GB, the 14B dense model hit 16.2 t/s, while Qwen3.6 35B NVFP4 reached 69.8 t/s.
Phosphene has released a local video/audio generation panel for Apple Silicon.
An MLX 4-bit conversion of Mistral Medium 3.5 128B has been released.
On Qwen3.6-35B-A3B, KLD and memory usage were compared across oQ, Q, MXFP, and UD MLX.
An MLX conversion of Gemma 4 31B mixed-quantized at 3 and 5 bits has been released.
Qwen3.6-27B was converted into a mixed 3-bit/5-bit quantization for MLX.
The quantization levels of MLX Q and oQ were compared using KL divergence and RAM usage.
Hunyuan3D-Paint and Shape were ported to MLX/MPS for Apple Silicon, making texture generation 3x faster.
Ported Hunyuan3D's image-to-mesh/texture pipeline to Apple Silicon.
Running the same prompt across 9 local models revealed a correlation between quantization provider differences and code length.
By sharing Wasm memory with the GPU, inference runs zero-copy on Apple Silicon.
Uniform quantization breaks Qwen3.5's sensitive layers.
SwiftLM presented results of running a 35B Qwen model on a 24GB Mac with 40K~100K context.
Ternary Bonsai has released 8B, 4B, and 1.7B models using 1.58-bit ternary weights.
PrismML has released Ternary Bonsai, a family of 8B, 4B, and 1.7B LLMs using 1.58-bit ternary weights.
Released a Skill and verification harness that helps port transformers to mlx-lm.
A summary of the performance and limitations of local music generation on Apple Silicon by running ACE-Step 1.5 with MLX.
A fast, uncensored local model optimized from Gemma 4 26B into MLX 4-bit.
In the latest oMLX commit, DFlash optimization significantly boosted tg TPS for small contexts.
On an M2 Max, DTree showed MLX results **1.07x** faster than DFlash.
Trained Qwen2.5-0.5B-Instruct on a Reddit summarization task using GRPO.
With DFlash in oMLX 0.3.5 RC1, Qwen3.5 27B went from 9 to 22 T/S on a Mac M5 Max.