ipex-llm/python/llm/example/GPU

IPEX-LLM Examples on Intel GPU

This folder contains examples of running IPEX-LLM on Intel GPU:

• Applications: running LLM applications (such as autogen) on IPEX-LLM
• HF-Transformers-AutoModels: running any Hugging Face Transformers model on IPEX-LLM (using the standard AutoModel APIs)
• LLM-Finetuning: running finetuning (such as LoRA, QLoRA, QA-LoRA, etc) using IPEX-LLM on Intel GPUs
• vLLM-Serving: running vLLM serving framework on intel GPUs (with IPEX-LLM low-bit optimized models)
• Deepspeed-AutoTP: running distributed inference using DeepSpeed AutoTP (with IPEX-LLM low-bit optimized models) on Intel GPUs
• Deepspeed-AutoTP-FastAPI: running distributed inference using DeepSpeed AutoTP and start serving with FastAPI(with IPEX-LLM low-bit optimized models) on Intel GPUs
• Pipeline-Parallel-Inference: running IPEX-LLM optimized low-bit model vertically partitioned on multiple Intel GPUs
• Pipeline-Parallel-FastAPI: running IPEX-LLM serving with FastAPI on multiple Intel GPUs in pipeline parallel fasion
• LangChain: running LangChain applications on IPEX-LLM
• PyTorch-Models: running any PyTorch model on IPEX-LLM (with "one-line code change")
• Speculative-Decoding: running any Hugging Face Transformers model with self-speculative decoding on Intel GPUs
• ModelScope-Models: running ModelScope model with IPEX-LLM on Intel GPUs
• Long-Context: running long-context generation with IPEX-LLM on Intel Arc™ A770 Graphics.
• StableDiffusion: running stable diffusion with IPEX-LLM on Intel GPUs.

System Support

1. Linux:

Hardware:

• Intel Arc™ A-Series Graphics
• Intel Data Center GPU Flex Series
• Intel Data Center GPU Max Series

Operating System:

• Ubuntu 20.04 or later (Ubuntu 22.04 is preferred)

2. Windows

Hardware:

• Intel iGPU and dGPU

Operating System:

• Windows 10/11, with or without WSL

Requirements

To apply Intel GPU acceleration, there’re several steps for tools installation and environment preparation. See the GPU installation guide for mode details.