PyTorch 2.15 brings FP8, faster LLM decoding and new Metal MPS kernels for Macs
PyTorch 2.15 (Hugging Face contributors) improves the MPS backend on Apple Silicon: adds torch.float8_e4m3fn (FP8) support with F.scaled_mm, faster single‑row linear and grouped matmul paths that speed Mixture‑of‑Experts decoding 5–6.5× (e.g., Qwen3.6‑35B‑A3B from 9.4→61.3 tok/s on an M3 Ultra), an ~8× faster softmax for sampling, and many new Metal kernels that cut driver memory for some video decoders (Wan2.2) from ~270 GiB to ~8 GiB. The release also raises platform requirements to macOS 15+ and Python 3.11+, and previews further MPSGraph→Metal migrations in 2.16.