# Run llama.cpp with IPEX-LLM on Intel GPU

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[ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp) provides fast LLM inference in pure C++ across a variety of hardware; you can now use the C++ interface of [`ipex-llm`](https://github.com/intel-analytics/ipex-llm) as an accelerated backend for `llama.cpp` running on Intel **GPU** *(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)*. > [!Important] > You may use [llama.cpp Portable Zip](./llamacpp_portable_zip_gpu_quickstart.md) to directly run llama.cpp on Intel GPU with ipex-llm (***without the need of manual installations***). > [!NOTE] > For installation on Intel Arc B-Series GPU (such as **B580**), please refer to this [guide](./bmg_quickstart.md). > [!NOTE] > Our latest version is consistent with [4ad2436](https://github.com/ggml-org/llama.cpp/commit/4ad2436) of llama.cpp. > > `ipex-llm[cpp]==2.2.0b20250629` is consistent with [d7cfe1f](https://github.com/ggml-org/llama.cpp/commit/d7cfe1ffe0f435d0048a6058d529daf76e072d9c) of llama.cpp. See the demo of running LLaMA2-7B on Intel Arc GPU below.

You could also click here to watch the demo video.

## Table of Contents - [Prerequisites](./llama_cpp_quickstart.md#0-prerequisites) - [Install IPEX-LLM for llama.cpp](./llama_cpp_quickstart.md#1-install-ipex-llm-for-llamacpp) - [Setup for running llama.cpp](./llama_cpp_quickstart.md#2-setup-for-running-llamacpp) - [Example: Running community GGUF models with IPEX-LLM](./llama_cpp_quickstart.md#3-example-running-community-gguf-models-with-ipex-llm) - [Troubleshooting](./llama_cpp_quickstart.md#troubleshooting) ## Quick Start This quickstart guide walks you through installing and running `llama.cpp` with `ipex-llm`. ### 0 Prerequisites IPEX-LLM's support for `llama.cpp` now is available for Linux system and Windows system. #### Linux For Linux system, we recommend Ubuntu 20.04 or later (Ubuntu 22.04 is preferred). Visit the [Install IPEX-LLM on Linux with Intel GPU](./install_linux_gpu.md), follow [Install Intel GPU Driver](./install_linux_gpu.md#install-gpu-driver) to install GPU driver for Ubuntu 22.04 (or follow the [Intel client GPU driver installation guide](https://dgpu-docs.intel.com/driver/client/overview.html) for a higher version of Ubuntu). And follow the guide [here](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html?packages=oneapi-toolkit&oneapi-toolkit-os=linux&oneapi-lin=offline) to install Intel® oneAPI Base Toolkit 2025.0. #### Windows (Optional) Please make sure your GPU driver version is equal or newer than `31.0.101.5522`. If it is not, follow the instructions in [this section](./install_windows_gpu.md#optional-update-gpu-driver) to update your GPU driver; otherwise, you might encounter gibberish output. ### 1. Install IPEX-LLM for llama.cpp To use `llama.cpp` with IPEX-LLM, first ensure that `ipex-llm[cpp]` is installed. - For **Linux users**: ```bash conda create -n llm-cpp python=3.11 conda activate llm-cpp pip install --pre --upgrade ipex-llm[cpp] ``` - For **Windows users**: Please run the following command in Miniforge Prompt. ```cmd conda create -n llm-cpp python=3.11 conda activate llm-cpp pip install --pre --upgrade ipex-llm[cpp] ``` **After the installation, you should have created a conda environment, named `llm-cpp` for instance, for running `llama.cpp` commands with IPEX-LLM.** ### 2. Setup for running llama.cpp First you should create a directory to use `llama.cpp`, for instance, use following command to create a `llama-cpp` directory and enter it. ```cmd mkdir llama-cpp cd llama-cpp ``` #### Initialize llama.cpp with IPEX-LLM Then you can use following command to initialize `llama.cpp` with IPEX-LLM: - For **Linux users**: ```bash init-llama-cpp ``` After `init-llama-cpp`, you should see many soft links of `llama.cpp`'s executable files and a `convert.py` in current directory. ![init_llama_cpp_demo_image](https://llm-assets.readthedocs.io/en/latest/_images/init_llama_cpp_demo_image.png) - For **Windows users**: Please run the following command with **administrator privilege in Miniforge Prompt**. ```cmd init-llama-cpp.bat ``` After `init-llama-cpp.bat`, you should see many soft links of `llama.cpp`'s executable files and a `convert.py` in current directory. ![init_llama_cpp_demo_image_windows](https://llm-assets.readthedocs.io/en/latest/_images/init_llama_cpp_demo_image_windows.png) > [!TIP] > `init-llama-cpp` will create soft links of llama.cpp's executable files to current directory, if you want to use these executable files in other places, don't forget to run above commands again. > [!NOTE] > If you have installed higher version `ipex-llm[cpp]` and want to upgrade your binary file, don't forget to remove old binary files first and initialize again with `init-llama-cpp` or `init-llama-cpp.bat`. **Now you can use these executable files by standard llama.cpp's usage.** #### Runtime Configuration To use GPU acceleration, several environment variables are required or recommended before running `llama.cpp`. - For **Linux users**: ```bash source /opt/intel/oneapi/setvars.sh export SYCL_CACHE_PERSISTENT=1 # [optional] under most circumstances, the following environment variable may improve performance, but sometimes this may also cause performance degradation export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 # [optional] if you want to run on single GPU, use below command to limit GPU may improve performance export ONEAPI_DEVICE_SELECTOR=level_zero:0 ``` - For **Windows users**: Please run the following command in Miniforge Prompt. ```cmd set SYCL_CACHE_PERSISTENT=1 rem under most circumstances, the following environment variable may improve performance, but sometimes this may also cause performance degradation set SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 ``` > [!TIP] > When your machine has multi GPUs and you want to run on one of them, you need to set `ONEAPI_DEVICE_SELECTOR=level_zero:[gpu_id]`, here `[gpu_id]` varies based on your requirement. For more details, you can refer to [this section](../Overview/KeyFeatures/multi_gpus_selection.md#2-oneapi-device-selector). > [!NOTE] > The environment variable `SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS` determines the usage of immediate command lists for task submission to the GPU. While this mode typically enhances performance, exceptions may occur. Please consider experimenting with and without this environment variable for best performance. For more details, you can refer to [this article](https://www.intel.com/content/www/us/en/developer/articles/guide/level-zero-immediate-command-lists.html). ### 3. Example: Running community GGUF models with IPEX-LLM Here we provide a simple example to show how to run a community GGUF model with IPEX-LLM. #### Model Download Before running, you should download or copy community GGUF model to your current directory. For instance, `mistral-7b-instruct-v0.1.Q4_K_M.gguf` of [Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/tree/main). #### Run the quantized model - For **Linux users**: ```bash ./llama-cli -m mistral-7b-instruct-v0.1.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" -c 1024 -t 8 -e -ngl 99 --color -no-cnv ``` > **Note**: > > For more details about meaning of each parameter, you can use `./llama-cli -h`. - For **Windows users**: Please run the following command in Miniforge Prompt. ```cmd llama-cli -m mistral-7b-instruct-v0.1.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" -c 1024 -t 8 -e -ngl 99 --color -no-cnv ``` > **Note**: > > For more details about meaning of each parameter, you can use `./llama-cli -h`. #### Sample Output ``` main: llama backend init main: load the model and apply lora adapter, if any llama_model_load_from_file_impl: using device SYCL0 (Intel(R) Arc(TM) A770 Graphics) - 15473 MiB free llama_model_loader: loaded meta data with 20 key-value pairs and 291 tensors from /home/arda/ruonan/mistral-7b-instruct-v0.1.Q4_K_M.gguf (version GGUF V2) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = llama llama_model_loader: - kv 1: general.name str = mistralai_mistral-7b-instruct-v0.1 llama_model_loader: - kv 2: llama.context_length u32 = 32768 llama_model_loader: - kv 3: llama.embedding_length u32 = 4096 llama_model_loader: - kv 4: llama.block_count u32 = 32 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336 llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 7: llama.attention.head_count u32 = 32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000 llama_model_loader: - kv 11: general.file_type u32 = 15 llama_model_loader: - kv 12: tokenizer.ggml.model str = llama llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["", "~~", "~~", "<0x00>", "<... llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ... llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2 llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 19: general.quantization_version u32 = 2 llama_model_loader: - type f32: 65 tensors llama_model_loader: - type q4_K: 193 tensors llama_model_loader: - type q6_K: 33 tensors print_info: file format = GGUF V2 print_info: file type = Q4_K - Medium print_info: file size = 4.07 GiB (4.83 BPW) load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect load: special tokens cache size = 3 load: token to piece cache size = 0.1637 MB print_info: arch = llama print_info: vocab_only = 0 print_info: n_ctx_train = 32768 print_info: n_embd = 4096 print_info: n_layer = 32 print_info: n_head = 32 print_info: n_head_kv = 8 print_info: n_rot = 128 print_info: n_swa = 0 print_info: n_embd_head_k = 128 print_info: n_embd_head_v = 128 print_info: n_gqa = 4 print_info: n_embd_k_gqa = 1024 print_info: n_embd_v_gqa = 1024 print_info: f_norm_eps = 0.0e+00 print_info: f_norm_rms_eps = 1.0e-05 print_info: f_clamp_kqv = 0.0e+00 print_info: f_max_alibi_bias = 0.0e+00 print_info: f_logit_scale = 0.0e+00 print_info: n_ff = 14336 print_info: n_expert = 0 print_info: n_expert_used = 0 print_info: causal attn = 1 print_info: pooling type = 0 print_info: rope type = 0 print_info: rope scaling = linear print_info: freq_base_train = 10000.0 print_info: freq_scale_train = 1 print_info: n_ctx_orig_yarn = 32768 print_info: rope_finetuned = unknown print_info: ssm_d_conv = 0 print_info: ssm_d_inner = 0 print_info: ssm_d_state = 0 print_info: ssm_dt_rank = 0 print_info: ssm_dt_b_c_rms = 0 print_info: model type = 7B print_info: model params = 7.24 B print_info: general.name = mistralai_mistral-7b-instruct-v0.1 print_info: vocab type = SPM print_info: n_vocab = 32000 print_info: n_merges = 0 print_info: BOS token = 1 '~~' print_info: EOS token = 2 '~~' print_info: UNK token = 0 '' print_info: LF token = 13 '<0x0A>' print_info: EOG token = 2 '' print_info: max token length = 48 load_tensors: loading model tensors, this can take a while... (mmap = true) load_tensors: offloading 32 repeating layers to GPU load_tensors: offloading output layer to GPU load_tensors: offloaded 33/33 layers to GPU load_tensors: CPU_Mapped model buffer size = 70.31 MiB load_tensors: SYCL0 model buffer size = 4095.05 MiB ................................................................................................. llama_init_from_model: n_seq_max = 1 llama_init_from_model: n_ctx = 1024 llama_init_from_model: n_ctx_per_seq = 1024 llama_init_from_model: n_batch = 1024 llama_init_from_model: n_ubatch = 1024 llama_init_from_model: flash_attn = 0 llama_init_from_model: freq_base = 10000.0 llama_init_from_model: freq_scale = 1 llama_init_from_model: n_ctx_per_seq (1024) < n_ctx_train (32768) -- the full capacity of the model will not be utilized Running with Environment Variables: GGML_SYCL_DEBUG: 0 GGML_SYCL_DISABLE_OPT: 1 Build with Macros: GGML_SYCL_FORCE_MMQ: no GGML_SYCL_F16: no Found 1 SYCL devices: | | | | |Max | |Max |Global | | | | | | |compute|Max work|sub |mem | | |ID| Device Type| Name|Version|units |group |group|size | Driver version| |--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------| | 0| [level_zero:gpu:0]| Intel Arc A770 Graphics| 12.55| 512| 1024| 32| 16225M| 1.6.31294.120000| SYCL Optimization Feature: |ID| Device Type|Reorder| |--|-------------------|-------| | 0| [level_zero:gpu:0]| Y| llama_kv_cache_init: kv_size = 1024, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 32, can_shift = 1 llama_kv_cache_init: SYCL0 KV buffer size = 128.00 MiB llama_init_from_model: KV self size = 128.00 MiB, K (f16): 64.00 MiB, V (f16): 64.00 MiB llama_init_from_model: SYCL_Host output buffer size = 0.12 MiB llama_init_from_model: SYCL0 compute buffer size = 164.01 MiB llama_init_from_model: SYCL_Host compute buffer size = 20.01 MiB llama_init_from_model: graph nodes = 902 llama_init_from_model: graph splits = 2 common_init_from_params: setting dry_penalty_last_n to ctx_size = 1024 common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable) main: llama threadpool init, n_threads = 8 system_info: n_threads = 8 (n_threads_batch = 8) / 32 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 | sampler seed: 403565315 sampler params: repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000 dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 1024 top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800 mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000 sampler chain: logits -> logit-bias -> penalties -> dry -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist generate: n_ctx = 1024, n_batch = 4096, n_predict = 32, n_keep = 1 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 exploring the world. But sometimes, she found it hard to find friends who shared her interests. One day, she decided to take matters into her own llama_perf_sampler_print: sampling time = x.xx ms / 63 runs ( x.xx ms per token, xx.xx tokens per second) llama_perf_context_print: load time = xx.xx ms llama_perf_context_print: prompt eval time = xx.xx ms / 31 tokens ( xx.xx ms per token, xx.xx tokens per second) llama_perf_context_print: eval time = xx.xx ms / 31 runs ( xx.xx ms per token, xx.xx tokens per second) llama_perf_context_print: total time = xx.xx ms / 62 tokens ``` ### Troubleshooting #### 1. Unable to run the initialization script If you are unable to run `init-llama-cpp.bat`, please make sure you have installed `ipex-llm[cpp]` in your conda environment. If you have installed it, please check if you have activated the correct conda environment. Also, if you are using Windows, please make sure you have run the script with administrator privilege in prompt terminal. #### 2. `DeviceList is empty. -30 (PI_ERROR_INVALID_VALUE)` error On Linux, this error happens when devices starting with `[ext_oneapi_level_zero]` are not found. Please make sure you have installed level-zero, and have sourced `/opt/intel/oneapi/setvars.sh` before running the command. #### 3. `Prompt is too long` error If you encounter `main: prompt is too long (xxx tokens, max xxx)`, please increase the `-c` parameter to set a larger size of context. #### 4. `gemm: cannot allocate memory on host` error / `could not create an engine` error If you meet `oneapi::mkl::oneapi::mkl::blas::gemm: cannot allocate memory on host` error, or `could not create an engine` on Linux, this is probably caused by pip installed OneAPI dependencies. You should prevent installing like `pip install dpcpp-cpp-rt==2024.0.2 mkl-dpcpp==2024.0.0 onednn==2024.0.0`, and instead use `apt` to install on Linux. Please refer to [this guide](./install_linux_gpu.md) for more details. #### 5. Fail to quantize model If you encounter `main: failed to quantize model from xxx`, please make sure you have created related output directory. #### 6. Program hang during model loading If your program hang after `llm_load_tensors: SYCL_Host buffer size = xx.xx MiB`, you can add `--no-mmap` in your command. #### 7. How to set `-ngl` parameter `-ngl` means the number of layers to store in VRAM. If your VRAM is enough, we recommend putting all layers on GPU, you can just set `-ngl` to a large number like 999 to achieve this goal. If `-ngl` is set to 0, it means that the entire model will run on CPU. If `-ngl` is set to greater than 0 and less than model layers, then it's mixed GPU + CPU scenario. #### 8. How to specificy GPU If your machine has multi GPUs, `llama.cpp` will default use all GPUs which may slow down your inference for model which can run on single GPU. You can add `-sm none` in your command to use one GPU only. Also, you can use `ONEAPI_DEVICE_SELECTOR=level_zero:[gpu_id]` to select device before excuting your command, more details can refer to [here](../Overview/KeyFeatures/multi_gpus_selection.md#2-oneapi-device-selector). #### 9. Program crash with Chinese prompt If you run the llama.cpp program on Windows and find that your program crashes or outputs abnormally when accepting Chinese prompts, you can search for `region` in the Windows search bar and go to `Region->Administrative->Change System locale..`, tick `Beta: Use Unicode UTF-8 for worldwide language support` option and then restart your computer. For detailed instructions on how to do this, see [this issue](https://github.com/intel-analytics/ipex-llm/issues/10989#issuecomment-2105598660). #### 10. sycl7.dll not found error If you meet `System Error: sycl7.dll not found` on Windows or you meet similar error on Linux, please check: 1. if you have installed conda and if you are in the right conda environment which has pip installed oneapi dependencies on Windows 2. if you have executed `source /opt/intel/oneapi/setvars.sh` on Linux #### 11. Check driver first when you meet garbage output on Windows If you meet garbage output on Windows, please check if your GPU driver version is >= [31.0.101.5522](https://www.intel.cn/content/www/cn/zh/download/785597/823163/intel-arc-iris-xe-graphics-windows.html). If not, please follow the instructions in [this section](./install_windows_gpu.md#optional-update-gpu-driver) to update your GPU driver. #### 12. Why my program can't find sycl device If you meet `GGML_ASSERT: C:/Users/Administrator/actions-runner/cpp-release/_work/llm.cpp/llm.cpp/llama-cpp-bigdl/ggml-sycl.cpp:18283: main_gpu_id