Video by PyTorch via YouTube
Ravi Gupta, Shiksha Patel, and Rishi Madduri (AMD) will present “Efficient Inference of Masked Diffusion Language Models on AMD XDNA2 NPU” at #PyTorchCon North America 2026.
Their poster presents a power-efficient deployment of LLaDA-8B-Instruct on an AMD NPU. On a Ryzen AI MAX+ 395 APU at sequence length 128, the NPU vendor path ran at 504 ms per forward at 36 W, compared with 843 ms and 110 W for CPU INT4, making it 1.67x faster and 8.9x better on energy. The poster will also cover the INT4 quantization and graph-partitioning pipeline, the CastAvx fix, and the runtime approach used for single-step prefill on the XDNA2 NPU.
Register for PyTorch Conference North America 2026: https://hubs.la/Q04v4SL60