ipex-llm
Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, DeepSeek, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc.) on Intel XPU (e.g., local PC with iGPU and NPU, discrete GPU such as Arc, Flex and Max); seamlessly integrate with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, vLLM, DeepSpeed, Axolotl, etc.
Stars: 7638
README:
< English | 中文 >
IPEX-LLM is an LLM acceleration library for Intel GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max), NPU and CPU 1.
[!NOTE]
IPEX-LLMprovides seamless integration with llama.cpp, Ollama, HuggingFace transformers, LangChain, LlamaIndex, vLLM, Text-Generation-WebUI, DeepSpeed-AutoTP, FastChat, Axolotl, HuggingFace PEFT, HuggingFace TRL, AutoGen, ModeScope, etc.- 70+ models have been optimized/verified on
ipex-llm(e.g., Llama, Phi, Mistral, Mixtral, DeepSeek, Qwen, ChatGLM, MiniCPM, Qwen-VL, MiniCPM-V and more), with state-of-art LLM optimizations, XPU acceleration and low-bit (FP8/FP6/FP4/INT4) support; see the complete list here.
- [2025/03] We added support for Gemma3 model in the latest llama.cpp Portable Zip.
- [2025/03] We can now run DeepSeek-R1-671B-Q4_K_M with 1 or 2 Arc A770 on Xeon using the latest llama.cpp Portable Zip.
- [2025/02] We added support of llama.cpp Portable Zip for Intel GPU (both Windows and Linux) and NPU (Windows only).
- [2025/02] We added support of Ollama Portable Zip to directly run Ollama on Intel GPU for both Windows and Linux (without the need of manual installations).
- [2025/02] We added support for running vLLM 0.6.6 on Intel Arc GPUs.
- [2025/01] We added the guide for running
ipex-llmon Intel Arc B580 GPU. - [2025/01] We added support for running Ollama 0.5.4 on Intel GPU.
- [2024/12] We added both Python and C++ support for Intel Core Ultra NPU (including 100H, 200V, 200K and 200H series).
More updates
- [2024/11] We added support for running vLLM 0.6.2 on Intel Arc GPUs.
- [2024/07] We added support for running Microsoft's GraphRAG using local LLM on Intel GPU; see the quickstart guide here.
- [2024/07] We added extensive support for Large Multimodal Models, including StableDiffusion, Phi-3-Vision, Qwen-VL, and more.
- [2024/07] We added FP6 support on Intel GPU.
- [2024/06] We added experimental NPU support for Intel Core Ultra processors; see the examples here.
- [2024/06] We added extensive support of pipeline parallel inference, which makes it easy to run large-sized LLM using 2 or more Intel GPUs (such as Arc).
- [2024/06] We added support for running RAGFlow with
ipex-llmon Intel GPU. - [2024/05]
ipex-llmnow supports Axolotl for LLM finetuning on Intel GPU; see the quickstart here. - [2024/05] You can now easily run
ipex-llminference, serving and finetuning using the Docker images. - [2024/05] You can now install
ipex-llmon Windows using just "one command". - [2024/04] You can now run Open WebUI on Intel GPU using
ipex-llm; see the quickstart here. - [2024/04] You can now run Llama 3 on Intel GPU using
llama.cppandollamawithipex-llm; see the quickstart here. - [2024/04]
ipex-llmnow supports Llama 3 on both Intel GPU and CPU. - [2024/04]
ipex-llmnow provides C++ interface, which can be used as an accelerated backend for running llama.cpp and ollama on Intel GPU. - [2024/03]
bigdl-llmhas now becomeipex-llm(see the migration guide here); you may find the originalBigDLproject here. - [2024/02]
ipex-llmnow supports directly loading model from ModelScope (魔搭). - [2024/02]
ipex-llmadded initial INT2 support (based on llama.cpp IQ2 mechanism), which makes it possible to run large-sized LLM (e.g., Mixtral-8x7B) on Intel GPU with 16GB VRAM. - [2024/02] Users can now use
ipex-llmthrough Text-Generation-WebUI GUI. - [2024/02]
ipex-llmnow supports Self-Speculative Decoding, which in practice brings ~30% speedup for FP16 and BF16 inference latency on Intel GPU and CPU respectively. - [2024/02]
ipex-llmnow supports a comprehensive list of LLM finetuning on Intel GPU (including LoRA, QLoRA, DPO, QA-LoRA and ReLoRA). - [2024/01] Using
ipex-llmQLoRA, we managed to finetune LLaMA2-7B in 21 minutes and LLaMA2-70B in 3.14 hours on 8 Intel Max 1550 GPU for Standford-Alpaca (see the blog here). - [2023/12]
ipex-llmnow supports ReLoRA (see "ReLoRA: High-Rank Training Through Low-Rank Updates"). - [2023/12]
ipex-llmnow supports Mixtral-8x7B on both Intel GPU and CPU. - [2023/12]
ipex-llmnow supports QA-LoRA (see "QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models"). - [2023/12]
ipex-llmnow supports FP8 and FP4 inference on Intel GPU. - [2023/11] Initial support for directly loading GGUF, AWQ and GPTQ models into
ipex-llmis available. - [2023/11]
ipex-llmnow supports vLLM continuous batching on both Intel GPU and CPU. - [2023/10]
ipex-llmnow supports QLoRA finetuning on both Intel GPU and CPU. - [2023/10]
ipex-llmnow supports FastChat serving on on both Intel CPU and GPU. - [2023/09]
ipex-llmnow supports Intel GPU (including iGPU, Arc, Flex and MAX). - [2023/09]
ipex-llmtutorial is released.
See demos of running local LLMs on Intel Core Ultra iGPU, Intel Core Ultra NPU, single-card Arc GPU, or multi-card Arc GPUs using ipex-llm below.
| Intel Core Ultra iGPU | Intel Core Ultra NPU | Intel Arc dGPU | 2-Card Intel Arc dGPUs |
|
|
|
|
|
Ollama (Mistral-7B, Q4_K) |
HuggingFace (Llama3.2-3B, SYM_INT4) |
TextGeneration-WebUI (Llama3-8B, FP8) |
llama.cpp (DeepSeek-R1-Distill-Qwen-32B, Q4_K) |
See the Token Generation Speed on Intel Core Ultra and Intel Arc GPU below1 (and refer to [2][3][4] for more details).
|
|
You may follow the Benchmarking Guide to run ipex-llm performance benchmark yourself.
Please see the Perplexity result below (tested on Wikitext dataset using the script here).
| Perplexity | sym_int4 | q4_k | fp6 | fp8_e5m2 | fp8_e4m3 | fp16 |
|---|---|---|---|---|---|---|
| Llama-2-7B-chat-hf | 6.364 | 6.218 | 6.092 | 6.180 | 6.098 | 6.096 |
| Mistral-7B-Instruct-v0.2 | 5.365 | 5.320 | 5.270 | 5.273 | 5.246 | 5.244 |
| Baichuan2-7B-chat | 6.734 | 6.727 | 6.527 | 6.539 | 6.488 | 6.508 |
| Qwen1.5-7B-chat | 8.865 | 8.816 | 8.557 | 8.846 | 8.530 | 8.607 |
| Llama-3.1-8B-Instruct | 6.705 | 6.566 | 6.338 | 6.383 | 6.325 | 6.267 |
| gemma-2-9b-it | 7.541 | 7.412 | 7.269 | 7.380 | 7.268 | 7.270 |
| Baichuan2-13B-Chat | 6.313 | 6.160 | 6.070 | 6.145 | 6.086 | 6.031 |
| Llama-2-13b-chat-hf | 5.449 | 5.422 | 5.341 | 5.384 | 5.332 | 5.329 |
| Qwen1.5-14B-Chat | 7.529 | 7.520 | 7.367 | 7.504 | 7.297 | 7.334 |
- Ollama: running Ollama on Intel GPU without the need of manual installations
- llama.cpp: running llama.cpp on Intel GPU without the need of manual installations
-
Arc B580: running
ipex-llmon Intel Arc B580 GPU for Ollama, llama.cpp, PyTorch, HuggingFace, etc. -
NPU: running
ipex-llmon Intel NPU in both Python/C++ or llama.cpp API. -
PyTorch/HuggingFace: running PyTorch, HuggingFace, LangChain, LlamaIndex, etc. (using Python interface of
ipex-llm) on Intel GPU for Windows and Linux -
vLLM: running
ipex-llmin vLLM on both Intel GPU and CPU -
FastChat: running
ipex-llmin FastChat serving on on both Intel GPU and CPU -
Serving on multiple Intel GPUs: running
ipex-llmserving on multiple Intel GPUs by leveraging DeepSpeed AutoTP and FastAPI -
Text-Generation-WebUI: running
ipex-llminoobaboogaWebUI -
Axolotl: running
ipex-llmin Axolotl for LLM finetuning -
Benchmarking: running (latency and throughput) benchmarks for
ipex-llmon Intel CPU and GPU
-
GPU Inference in C++: running
llama.cpp,ollama, etc., withipex-llmon Intel GPU -
GPU Inference in Python : running HuggingFace
transformers,LangChain,LlamaIndex,ModelScope, etc. withipex-llmon Intel GPU -
vLLM on GPU: running
vLLMserving withipex-llmon Intel GPU -
vLLM on CPU: running
vLLMserving withipex-llmon Intel CPU -
FastChat on GPU: running
FastChatserving withipex-llmon Intel GPU -
VSCode on GPU: running and developing
ipex-llmapplications in Python using VSCode on Intel GPU
-
GraphRAG: running Microsoft's
GraphRAGusing local LLM withipex-llm -
RAGFlow: running
RAGFlow(an open-source RAG engine) withipex-llm -
LangChain-Chatchat: running
LangChain-Chatchat(Knowledge Base QA using RAG pipeline) withipex-llm -
Coding copilot: running
Continue(coding copilot in VSCode) withipex-llm -
Open WebUI: running
Open WebUIwithipex-llm -
PrivateGPT: running
PrivateGPTto interact with documents withipex-llm -
Dify platform: running
ipex-llminDify(production-ready LLM app development platform)
-
Windows GPU: installing
ipex-llmon Windows with Intel GPU -
Linux GPU: installing
ipex-llmon Linux with Intel GPU - For more details, please refer to the full installation guide
-
- INT4 inference: INT4 LLM inference on Intel GPU and CPU
- FP8/FP6/FP4 inference: FP8, FP6 and FP4 LLM inference on Intel GPU
- INT8 inference: INT8 LLM inference on Intel GPU and CPU
- INT2 inference: INT2 LLM inference (based on llama.cpp IQ2 mechanism) on Intel GPU
-
- FP16 LLM inference on Intel GPU, with possible self-speculative decoding optimization
- BF16 LLM inference on Intel CPU, with possible self-speculative decoding optimization
-
-
Low-bit models: saving and loading
ipex-llmlow-bit models (INT4/FP4/FP6/INT8/FP8/FP16/etc.) -
GGUF: directly loading GGUF models into
ipex-llm -
AWQ: directly loading AWQ models into
ipex-llm -
GPTQ: directly loading GPTQ models into
ipex-llm
-
Low-bit models: saving and loading
- Tutorials
Over 70 models have been optimized/verified on ipex-llm, including LLaMA/LLaMA2, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM2/ChatGLM3, Baichuan/Baichuan2, Qwen/Qwen-1.5, InternLM and more; see the list below.
| Model | CPU Example | GPU Example | NPU Example |
|---|---|---|---|
| LLaMA | link1, link2 | link | |
| LLaMA 2 | link1, link2 | link | Python link, C++ link |
| LLaMA 3 | link | link | Python link, C++ link |
| LLaMA 3.1 | link | link | |
| LLaMA 3.2 | link | Python link, C++ link | |
| LLaMA 3.2-Vision | link | ||
| ChatGLM | link | ||
| ChatGLM2 | link | link | |
| ChatGLM3 | link | link | |
| GLM-4 | link | link | |
| GLM-4V | link | link | |
| GLM-Edge | link | Python link | |
| GLM-Edge-V | link | ||
| Mistral | link | link | |
| Mixtral | link | link | |
| Falcon | link | link | |
| MPT | link | link | |
| Dolly-v1 | link | link | |
| Dolly-v2 | link | link | |
| Replit Code | link | link | |
| RedPajama | link1, link2 | ||
| Phoenix | link1, link2 | ||
| StarCoder | link1, link2 | link | |
| Baichuan | link | link | |
| Baichuan2 | link | link | Python link |
| InternLM | link | link | |
| InternVL2 | link | ||
| Qwen | link | link | |
| Qwen1.5 | link | link | |
| Qwen2 | link | link | Python link, C++ link |
| Qwen2.5 | link | Python link, C++ link | |
| Qwen-VL | link | link | |
| Qwen2-VL | link | ||
| Qwen2-Audio | link | ||
| Aquila | link | link | |
| Aquila2 | link | link | |
| MOSS | link | ||
| Whisper | link | link | |
| Phi-1_5 | link | link | |
| Flan-t5 | link | link | |
| LLaVA | link | link | |
| CodeLlama | link | link | |
| Skywork | link | ||
| InternLM-XComposer | link | ||
| WizardCoder-Python | link | ||
| CodeShell | link | ||
| Fuyu | link | ||
| Distil-Whisper | link | link | |
| Yi | link | link | |
| BlueLM | link | link | |
| Mamba | link | link | |
| SOLAR | link | link | |
| Phixtral | link | link | |
| InternLM2 | link | link | |
| RWKV4 | link | ||
| RWKV5 | link | ||
| Bark | link | link | |
| SpeechT5 | link | ||
| DeepSeek-MoE | link | ||
| Ziya-Coding-34B-v1.0 | link | ||
| Phi-2 | link | link | |
| Phi-3 | link | link | |
| Phi-3-vision | link | link | |
| Yuan2 | link | link | |
| Gemma | link | link | |
| Gemma2 | link | ||
| DeciLM-7B | link | link | |
| Deepseek | link | link | |
| StableLM | link | link | |
| CodeGemma | link | link | |
| Command-R/cohere | link | link | |
| CodeGeeX2 | link | link | |
| MiniCPM | link | link | Python link, C++ link |
| MiniCPM3 | link | ||
| MiniCPM-V | link | ||
| MiniCPM-V-2 | link | link | |
| MiniCPM-Llama3-V-2_5 | link | Python link | |
| MiniCPM-V-2_6 | link | link | Python link |
| MiniCPM-o-2_6 | link | ||
| Janus-Pro | link | ||
| Moonlight | link | ||
| StableDiffusion | link | ||
| Bce-Embedding-Base-V1 | Python link | ||
| Speech_Paraformer-Large | Python link |
- Please report a bug or raise a feature request by opening a Github Issue
- Please report a vulnerability by opening a draft GitHub Security Advisory
-
Performance varies by use, configuration and other factors.
ipex-llmmay not optimize to the same degree for non-Intel products. Learn more at www.Intel.com/PerformanceIndex. ↩ ↩2
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for ipex-llm
Similar Open Source Tools
ipex-llm
IPEX-LLM is a PyTorch library for running Large Language Models (LLMs) on Intel CPUs and GPUs with very low latency. It provides seamless integration with various LLM frameworks and tools, including llama.cpp, ollama, Text-Generation-WebUI, HuggingFace transformers, and more. IPEX-LLM has been optimized and verified on over 50 LLM models, including LLaMA, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM, Baichuan, Qwen, and RWKV. It supports a range of low-bit inference formats, including INT4, FP8, FP4, INT8, INT2, FP16, and BF16, as well as finetuning capabilities for LoRA, QLoRA, DPO, QA-LoRA, and ReLoRA. IPEX-LLM is actively maintained and updated with new features and optimizations, making it a valuable tool for researchers, developers, and anyone interested in exploring and utilizing LLMs.
dora
Dataflow-oriented robotic application (dora-rs) is a framework that makes creation of robotic applications fast and simple. Building a robotic application can be summed up as bringing together hardwares, algorithms, and AI models, and make them communicate with each others. At dora-rs, we try to: make integration of hardware and software easy by supporting Python, C, C++, and also ROS2. make communication low latency by using zero-copy Arrow messages. dora-rs is still experimental and you might experience bugs, but we're working very hard to make it stable as possible.
agentica
Agentica is a human-centric framework for building large language model agents. It provides functionalities for planning, memory management, tool usage, and supports features like reflection, planning and execution, RAG, multi-agent, multi-role, and workflow. The tool allows users to quickly code and orchestrate agents, customize prompts, and make API calls to various services. It supports API calls to OpenAI, Azure, Deepseek, Moonshot, Claude, Ollama, and Together. Agentica aims to simplify the process of building AI agents by providing a user-friendly interface and a range of functionalities for agent development.
chatluna
Chatluna is a machine learning model plugin that provides chat services with large language models. It is highly extensible, supports multiple output formats, and offers features like custom conversation presets, rate limiting, and context awareness. Users can deploy Chatluna under Koishi without additional configuration. The plugin supports various models/platforms like OpenAI, Azure OpenAI, Google Gemini, and more. It also provides preset customization using YAML files and allows for easy forking and development within Koishi projects. However, the project lacks web UI, HTTP server, and project documentation, inviting contributions from the community.
web-builder
Web Builder is a low-code front-end framework based on Material for Angular, offering a rich component library for excellent digital innovation experience. It allows rapid construction of modern responsive UI, multi-theme, multi-language web pages through drag-and-drop visual configuration. The framework includes a beautiful admin theme, complete front-end solutions, and AI integration in the Pro version for optimizing copy, creating components, and generating pages with a single sentence.
vlmrun-cookbook
VLM Run Cookbook is a repository containing practical examples and tutorials for extracting structured data from images, videos, and documents using Vision Language Models (VLMs). It offers comprehensive Colab notebooks demonstrating real-world applications of VLM Run, with complete code and documentation for easy adaptation. The examples cover various domains such as financial documents and TV news analysis.
DownEdit
DownEdit is a fast and powerful program for downloading and editing videos from platforms like TikTok, Douyin, and Kuaishou. It allows users to effortlessly grab videos, make bulk edits, and utilize advanced AI features for generating videos, images, and sounds in bulk. The tool offers features like video, photo, and sound editing, downloading videos without watermarks, bulk AI generation, and AI editing for content enhancement.
DownEdit
DownEdit is a powerful program that allows you to download videos from various social media platforms such as TikTok, Douyin, Kuaishou, and more. With DownEdit, you can easily download videos from user profiles and edit them in bulk. You have the option to flip the videos horizontally or vertically throughout the entire directory with just a single click. Stay tuned for more exciting features coming soon!
ASTRA.ai
Astra.ai is a multimodal agent powered by TEN, showcasing its capabilities in speech, vision, and reasoning through RAG from local documentation. It provides a platform for developing AI agents with features like RTC transportation, extension store, workflow builder, and local deployment. Users can build and test agents locally using Docker and Node.js, with prerequisites including Agora App ID, Azure's speech-to-text and text-to-speech API keys, and OpenAI API key. The platform offers advanced customization options through config files and API keys setup, enabling users to create and deploy their AI agents for various tasks.
PocketFlow
Pocket Flow is a 100-line minimalist LLM framework designed for (Multi-)Agents, Workflow, RAG, etc. It provides a core abstraction for LLM projects by focusing on computation and communication through a graph structure and shared store. The framework aims to support the development of LLM Agents, such as Cursor AI, by offering a minimal and low-level approach that is well-suited for understanding and usage. Users can install Pocket Flow via pip or by copying the source code, and detailed documentation is available on the project website.
bytedesk
Bytedesk is an AI-powered customer service and team instant messaging tool that offers features like enterprise instant messaging, online customer service, large model AI assistant, and local area network file transfer. It supports multi-level organizational structure, role management, permission management, chat record management, seating workbench, work order system, seat management, data dashboard, manual knowledge base, skill group management, real-time monitoring, announcements, sensitive words, CRM, report function, and integrated customer service workbench services. The tool is designed for team use with easy configuration throughout the company, and it allows file transfer across platforms using WiFi/hotspots without the need for internet connection.
PureChat
PureChat is a chat application integrated with ChatGPT, featuring efficient application building with Vite5, screenshot generation and copy support for chat records, IM instant messaging SDK for sessions, automatic light and dark mode switching based on system theme, Markdown rendering, code highlighting, and link recognition support, seamless social experience with GitHub quick login, integration of large language models like ChatGPT Ollama for streaming output, preset prompts, and context, Electron desktop app versions for macOS and Windows, ongoing development of more features. Environment setup requires Node.js 18.20+. Clone code with 'git clone https://github.com/Hyk260/PureChat.git', install dependencies with 'pnpm install', start project with 'pnpm dev', and build with 'pnpm build'.
cursor-free-vip
Cursor Free VIP is a tool designed to automatically bypass Cursor's membership check, upgrade to 'pro' membership, support Windows and macOS systems, send Token requests in real-time, and reset Cursor's configuration. It provides a seamless experience for users to access premium features without the need for manual upgrades or configuration changes. The tool aims to simplify the process of accessing advanced functionalities offered by Cursor, enhancing user experience and productivity.
airbyte-connectors
This repository contains Airbyte connectors used in Faros and Faros Community Edition platforms as well as Airbyte Connector Development Kit (CDK) for JavaScript/TypeScript.