February 05, 2025
Enabling advanced GPU features in PyTorch - Warp Specialization
Meta: Hongtao Yu, Manman Ren, Bert Maher, Shane Nay NVIDIA: Gustav Zhu, Shuhao Jiang
January 29, 2025
PyTorch 2.6 Release Blog
We are excited to announce the release of PyTorch® 2.6 (release notes)! This release features multiple improvements for PT2: torch.compile can now be used with Python 3.13; new performance-related knob torch.compiler.set_stance; several AOTInductor enhancements. Besides the PT2 improvements, another highlight is FP16 support on X86 CPUs.
January 24, 2025
How Intel Uses PyTorch to Empower Generative AI through Intel Arc GPUs
Intel has long been at the forefront of technological innovation, and its recent venture into Generative AI (GenAI) solutions is no exception. With the rise of AI-powered gaming experiences, Intel sought to deliver an accessible and intuitive GenAI inferencing solution tailored for AI PCs powered by Intel’s latest GPUs. By leveraging PyTorch as the backbone for development efforts, Intel successfully launched AI Playground, an open source application that showcases advanced GenAI workloads.
January 21, 2025
Accelerating LLM Inference with GemLite, TorchAO and SGLang
Large Language Models (LLMs) are typically very resource-intensive, requiring significant amounts of memory, compute and power to operate effectively. Quantization provides a solution by reducing weights and activations from 16 bit floats to lower bitrates (e.g., 8 bit, 4 bit, 2 bit), achieving significant speedup and memory savings and also enables support for larger batch sizes.
January 14, 2025
GenAI Acceleration for PyTorch 2.5 on Intel® Xeon®Processors
This blog is the fifth in a series focused on accelerating generative AI models with pure, native PyTorch. We demonstrate the GenAI acceleration of GPTFast, Segment Anything Fast, and Diffusion Fast on Intel® Xeon®Processors.
January 09, 2025
Integrating Ascend Backend with Torchtune through PyTorch Multi-Device Support
In this blog, we will briefly introduce torchtune, the Ascend backend, and demonstrate how torchtune can be used to fine-tune models with Ascend.
