xpu

A high-performance framework for training LLMs, VLMs, diffusion, and embodied models on NVIDIA GPUs and Kunlun XPUs.

Package for writing high-level code for parallel high-performance stencil computations that can be deployed on both GPUs and CPUs

A scheduling framework for multitasking over diverse XPUs, including GPUs, NPUs, ASICs, and FPGAs

🎨ComfyUI standalone pack for Intel GPUs. | 英特尔显卡 ComfyUI 整合包

Self-contained Python lib with zero-dependencies that give you a unified device properties for gpu, cpu, and npu. No more calling separate tools such as nvidia-smi or /proc/cpuinfo and parsing it yourself.

Purplecoin/XPU Core integration/staging tree

Automatic Triton kernel generation and optimization for Intel GPU, powered by Claude Code.

ACE-Step 1.5 Local AI Music Generation App on XPU (Intel GPU)

Template for starting PyTorch projects with Intel GPUs (XPU) on Windows.

videogen-b70: ComfyUI + PyTorch XPU recipes for MiniMax H3 T2V on Intel Arc Pro B70. Auto-detects one or two cards (CLIP on GPU1, UNet on GPU0). Not tensor parallel.

Windows Capture GPU tensor version for AMD ROCm, NVIDIA CUDA and Intel XPU backends

Fast local voice cloning with Qwen3-TTS on affordable Intel Arc GPUs using IPEX. No CUDA. No cloud. Zero cost.

A MIDI generator based on the Transformer model. Allows you to train your own model and test it.

Running LTX‑2 19B AI Model: Image/Audio-to-Video Locally on Intel Arc (XPU) GPU & CPU

Docker Compose stack for serving a local, OpenAI-compatible LLM (vLLM on Intel XPU) on an Intel Arc Pro B60 GPU — reproducible config with operator and developer docs.

This repository is used to track the learning process for XPU GEMM.

Gradio-Powered Handwriting (TrOCR + OpenVINO) to Video Generation (LTX + OpenVINO GenAI)

HQH-XPU: Intel Arc A770 ComfyUI optimization bundle — TINT4 (oneDNN hybrid), OmniXPU, XPU-CacheClean, ClipProj (XPU) & UniversalIO; one install auto-deploys all nodes.

Sentiment classification app built with RoBERTa and optimized using Intel OpenVINO for deployment on Intel XPU devices. This project demonstrates how large language models (LLMs) can be fine-tuned and accelerated for real-time inference, making it ideal for low-latency AI applications.

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