mlx

Highlights

• Much faster bandwidth with JACCL on macOS >= 26.3 (some numbers)

What's Changed

• patch by @awni in https://github.com/ml-explore/mlx/pull/3093
• Disable managed memory on WSL when concurrentManagedAccess is not supported by @jessegross in https://github.com/ml-explore/mlx/pull/3095
• Fix non simd f16 build by @awni in http...

What's Changed

• patch by @awni in https://github.com/ml-explore/mlx/pull/3074
• [CUDA] Fallback Event impl when there is no hardware cpu/gpu coherency by @zcbenz in https://github.com/ml-explore/mlx/pull/3070
• Tune CUDA gaph sizes on B200 and H100 by @awni in https://github.com/ml-explore/mlx/pull/3077
• [Docs] Simple example of using MLX distributed by @stefpi in https://github.com/ml-ex...

Highlights

• Metal: Much faster vector fused grouped-query attention for long context
• CUDA: Several improvements to speed up LLM inference for CUDA backend
• CUDA: Support for dense MoEs
• CUDA: Better support for consumer GPUs (4090, 5090, RTX 6000, ...)

What's Changed

• patch bump for next release by @awni in https://github.com/ml-explore/mlx/pull/2991
• Fix fence by @awni in h...

Highlights

• Support nvfp4 and mxfp8 quantized ops on Metal
• Support nvfp4 and mxfp8 quantized-quantized matrix-matrix multiplication on CUDA

What's Changed

• Bump the patch version by @angeloskath in https://github.com/ml-explore/mlx/pull/2922
• Faster copy for col contig to row contig by @awni in https://github.com/ml-explore/mlx/pull/2917
• Fix cuda release by @awni in https:...

Highlights

• RDMA over thunderbolt with the JACCL backend (macOS >= 26.2) (some numbers)
• NAX with JIT so that they can be used in MLX Swift
• CUDA improvements
  • Many improvements to SDPA (masking, T_q != T_kv)
  • Faster quantize/dequantize
  • QQMM to make use of faster tensor cores
  • Fix in col reduce speeds up training

What's...