This folder contains examples of running IPEX-LLM on Intel GPU:
- Applications: running LLM applications (such as autogen) on IPEX-LLM
- HuggingFace: running HuggingFace models on IPEX-LLM (using the standard AutoModel APIs), including language models and multimodal models.
- 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-Serving: running IPEX-LLM serving with FastAPI on multiple Intel GPUs in pipeline parallel fasion
- Lightweight-Serving: running IPEX-LLM serving with FastAPI on one Intel GPU In a lightweight way
- 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.
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)
Hardware:
- Intel iGPU and dGPU
Operating System:
- Windows 10/11, with or without WSL
To apply Intel GPU acceleration, there’re several steps for tools installation and environment preparation. See the GPU installation guide for mode details.