AMD highlighted AI training, inference, and performance optimization at PyTorch Conference 2026, emphasizing ROCm software and PyTorch integration.
At the PyTorch Conference 2026, held October 20–21 in San Jose, AMD demonstrated its commitment to advancing the open AI ecosystem through innovations in training, inference, and performance optimization. The company emphasized its contributions to the ROCm™ software stack and its support for scalable, efficient AI applications across diverse platforms.
AMD showcased native PyTorch integration across its ROCm software stack, enabling seamless development on cloud, data center, workstation, and client platforms. Key highlights included the MLIR-native FlyDSL backend for TorchInductor, reinforcement learning post-training and rollout, and PyTorch inference on AMD XDNA™ NPU architecture. Hands-on learning sessions through AMD Skills further empowered developers to optimize their AI workflows.
Performance optimization was a central theme of AMD's contributions. The company presented advancements such as PerfModel for Triton kernel optimization, Grouped GEMM on ROCm, TorchAO kernels with MXFP8, FlexAttention and KV cache optimization, and inference analysis using TraceLens. Other efforts included agentic workload optimization with Hyperloom and efficient masked diffusion language model inference.
AMD highlighted its progress in scaling AI training and inference workloads. This included MXFP8 pretraining across over 1,000 AMD Instinct™ MI355X GPUs, distributed training with TorchTitan, and innovations like rocSHMEM symmetric memory and RCCL GPU-initiated networking. Additional advancements included MoRI and vLLM for expert parallelism and disaggregated Mixture of Experts serving, as well as large-scale reinforcement learning training and rollout.
AMD engineers delivered breakout sessions, lightning talks, and posters at the conference.
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