ipex-llm/python/llm/example/GPU
Jin Qiao 440cfe18ed LLM: GPU Example Updates for Windows (#9992)
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Deepspeed-AutoTP LLM: add avg token latency information and benchmark guide of autotp (#9940) 2024-01-19 15:09:57 +08:00
HF-Transformers-AutoModels LLM: GPU Example Updates for Windows (#9992) 2024-01-29 11:25:11 +08:00
LLM-Finetuning LLM: reorganize GPU finetuning examples (#9952) 2024-01-25 19:02:38 +08:00
PyTorch-Models LLM: GPU Example Updates for Windows (#9992) 2024-01-29 11:25:11 +08:00
Speculative-Decoding LLM: Add qwen CPU speculative example (#9985) 2024-01-25 17:01:34 +08:00
vLLM-Serving Update llm gpu xpu default related info to PyTorch 2.1 (#9866) 2024-01-09 15:38:47 +08:00
README.md LLM: reorganize GPU finetuning examples (#9952) 2024-01-25 19:02:38 +08:00

BigDL-LLM Examples on Intel GPU

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

  • HF-Transformers-AutoModels: running any Hugging Face Transformers model on BigDL-LLM (using the standard AutoModel APIs)
  • LLM-Finetuning: running finetuning (such as LoRA, QLoRA, QA-LoRA, etc) using BigDL-LLM on Intel GPUs
  • vLLM-Serving: running vLLM serving framework on intel GPUs (with BigDL-LLM low-bit optimized models)
  • Deepspeed-AutoTP: running distributed inference using DeepSpeed AutoTP (with BigDL-LLM low-bit optimized models) on Intel GPUs
  • PyTorch-Models: running any PyTorch model on BigDL-LLM (with "one-line code change")

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, therere several steps for tools installation and environment preparation. See the GPU installation guide for mode details.