From the source
Hugging Face's PyTorch team says PyTorch 2.15 brings FP8 tensors, faster language-model decoding and new Metal kernels to the MPS backend for
Apple silicon Macs.
- Mixture-of-experts models decode 5–6.5× faster than in 2.14: on an M3 Ultra with torch.compile, Qwen3.6-35B-A3B goes from 9.4 to 61.3 tokens/s and LFM2.5-8B-A1B from 23.6 to 123.6 tokens/s.
- Dense models gain from matrix-vector kernels wired into F.linear, a 1.23× geometric-mean speedup across 96 BF16 layer shapes, and softmax over the vocabulary is about 8× faster.
- FP8 adds torch.float8_e4m3fn tensors at one byte per value, half the storage of FP16 or BF16, multiplied on the GPU with F.scaled_mm using tensorwise or rowwise scaling.
- A padding fix repairs the Qwen-Image-2.1 VAE encoder, which silently corrupted images on MPS in 2.14; an encode-decode round trip now matches CPU at 29.95 dB PSNR, up from 10.06 dB.
