# PyTorch 2.15

PyTorch 2.15 makes the rewritten NCCL backend, previewed as opt-in nccl2 in 2.14, the default for every nccl process group, keeping TORCH_DIST_USE_NCCL2=0 and the nccl-legacy name as rollback paths. At cluster scale a single stalled GPU could halt a whole training run and the cause was often reproduced by hand; a backend-agnostic flight recorder now writes a post-mortem to disk when a collective hangs, and a timeout on any rank triggers dumps from every rank.

PyTorch 2.15 makes the rewritten NCCL backend, previewed as opt-in nccl2 in 2.14, the default for every nccl process group, keeping TORCH_DIST_USE_NCCL2=0 and the nccl-legacy name as rollback paths. At cluster scale a single stalled GPU could halt a whole training run and the cause was often reproduced by hand; a backend-agnostic flight recorder now writes a post-mortem to disk when a collective hangs, and a timeout on any rank triggers dumps from every rank.

PyTorch 2.15 brings FP8 tensors, a native grouped matmul and a batch of dedicated Metal kernels to the Mac's MPS backend, making local LLM decoding and video generation faster and lighter on memory. Previously many MPS operators went through MPSGraph, MoE decoding copied group offsets to the CPU and looped over groups, and tiled video VAE decoding pushed driver memory to 270 GiB. On an M3 Ultra, Qwen3.6-35B-A3B decoding rises from 9.4 to 61.3 tokens/s, and Wan2.2 VAE decoding peaks near 8 GiB.

PyTorch 2.15 brings FP8 tensors, a native grouped matmul and a batch of dedicated Metal kernels to the Mac's MPS backend, making local LLM decoding and video generation faster and lighter on memory. Previously many MPS operators went through MPSGraph, MoE decoding copied group offsets to the CPU and looped over groups, and tiled video VAE decoding pushed driver memory to 270 GiB. On an M3 Ultra, Qwen3.6-35B-A3B decoding rises from 9.4 to 61.3 tokens/s, and Wan2.2 VAE decoding peaks near 8 GiB.