Run Llama 3 on Intel GPU using llama.cpp and ollama with IPEX-LLM

Llama 3 is the latest Large Language Models released by Meta which provides state-of-the-art performance and excels at language nuances, contextual understanding, and complex tasks like translation and dialogue generation.

Now, you can easily run Llama 3 on Intel GPU using llama.cpp and Ollama with IPEX-LLM.

See the demo of running Llama-3-8B-Instruct on Intel Arc GPU using Ollama below.

Quick Start

This quickstart guide walks you through how to run Llama 3 on Intel GPU using llama.cpp / Ollama with IPEX-LLM.

1. Run Llama 3 using llama.cpp

1.1 Install IPEX-LLM for llama.cpp and Initialize

Visit Run llama.cpp with IPEX-LLM on Intel GPU Guide, and follow the instructions in section Prerequisites to setup and section Install IPEX-LLM for llama.cpp to install the IPEX-LLM with llama.cpp binaries, then follow the instructions in section Initialize llama.cpp with IPEX-LLM to initialize.

After above steps, you should have created a conda environment, named llm-cpp for instance and have llama.cpp binaries in your current directory.

Now you can use these executable files by standard llama.cpp usage.

1.2 Download Llama3

There already are some GGUF models of Llama3 in community, here we take Meta-Llama-3-8B-Instruct-GGUF for example.

Suppose you have downloaded a Meta-Llama-3-8B-Instruct-Q4_K_M.gguf model from Meta-Llama-3-8B-Instruct-GGUF and put it under <model_dir>.

1.3 Run Llama3 on Intel GPU using llama.cpp

Set Environment Variables

Configure oneAPI variables by running the following command:

LinuxWindows

source /opt/intel/oneapi/setvars.sh

Run llama3

Under your current directory, exceuting below command to do inference with Llama3:

LinuxWindows

./main -m <model_dir>/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf -n 32 --prompt "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun doing something" -t 8 -e -ngl 33 --color --no-mmap

Under your current directory, you can also execute below command to have interactive chat with Llama3:

LinuxWindows

./main -ngl 33 -c 0 --interactive-first --color -e --in-prefix '<|start_header_id|>user<|end_header_id|>\n\n' --in-suffix '<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n' -r '<|eot_id|>' -m <model_dir>/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf

Below is a sample output on Intel Arc GPU:

2. Run Llama3 using Ollama

2.1 Install IPEX-LLM for Ollama and Initialize

Visit Run Ollama with IPEX-LLM on Intel GPU, and follow the instructions in section Install IPEX-LLM for llama.cpp to install the IPEX-LLM with Ollama binary, then follow the instructions in section Initialize Ollama to initialize.

After above steps, you should have created a conda environment, named llm-cpp for instance and have ollama binary file in your current directory.

Now you can use this executable file by standard Ollama usage.

2.2 Run Llama3 on Intel GPU using Ollama

ollama/ollama has alreadly added Llama3 into its library, so it’s really easy to run Llama3 using ollama now.

2.2.1 Run Ollama Serve

Launch the Ollama service:

LinuxWindows

export no_proxy=localhost,127.0.0.1
export ZES_ENABLE_SYSMAN=1
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export OLLAMA_NUM_GPU=999
source /opt/intel/oneapi/setvars.sh

./ollama serve

Note

To allow the service to accept connections from all IP addresses, use OLLAMA_HOST=0.0.0.0 ./ollama serve instead of just ./ollama serve.

2.2.2 Using Ollama Run Llama3

Keep the Ollama service on and open another terminal and run llama3 with ollama run:

LinuxWindows

export no_proxy=localhost,127.0.0.1
./ollama run llama3:8b-instruct-q4_K_M

Note

Here we just take llama3:8b-instruct-q4_K_M for example, you can replace it with any other Llama3 model you want.

Below is a sample output on Intel Arc GPU :