MPS

Headlines · 2

1. 3d
   PyTorch 2.15 ships new distributed backend and low-precision inference updates

   PyTorch Blog announced PyTorch 2.15, making the rewritten NCCL backend the default for every NCCL process group while retaining legacy rollback options. The release also adds one-sided torch.distributed transports, FSDP2 and pipelining controls, faster NVFP4 decoding on NVIDIA Blackwell, and FP8 and decode optimizations for Apple Silicon. Platform support expands to Python 3.11–3.15, CUDA 13.2 and 13.4, ROCm 10.0, AMD, and Intel GPUs.

   PyTorch Blog · 🔥 0
2. Oct 20
   PyTorch 2.15 MPS adds FP8 and faster LLM decoding on Macs

   Hugging Face's PyTorch team published a blog post on October 20, 2026 describing MPS improvements in PyTorch 2.15, including FP8 support with float8_e4m3fn storage and scaled matrix multiplication on the Mac backend. The team reports self-measured gains: MoE decoding 5–6.5× faster than 2.14, with Qwen3.6-35B-A3B rising from 9.4 to 61.3 tokens/s on an M3 Ultra, and Wan2.2 video VAE decode peak memory falling from 270 GiB to about 8 GiB. The post also says MPS now requires macOS 15 or later, PyTorch 2.15 requires Python 3.11 or later, and 2.16 will run attention entirely on Metal.

   Hugging Face · 🔥 0

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