⚡ GPU acceleration

This section contains instruction on how to use LocalAI with GPU acceleration.

Model configuration link

Depending on the model architecture and backend used, there might be different ways to enable GPU acceleration. It is required to configure the model you intend to use with a YAML config file. For example, for llama.cpp workloads a configuration file might look like this (where gpu_layers is the number of layers to offload to the GPU):

  name: my-model-name
# Default model parameters
parameters:
  # Relative to the models path
  model: llama.cpp-model.ggmlv3.q5_K_M.bin

context_size: 1024
threads: 1

f16: true # enable with GPU acceleration
gpu_layers: 22 # GPU Layers (only used when built with cublas)
  

For diffusers instead, it might look like this instead:

  name: stablediffusion
parameters:
  model: toonyou_beta6.safetensors
backend: diffusers
step: 30
f16: true
diffusers:
  pipeline_type: StableDiffusionPipeline
  cuda: true
  enable_parameters: "negative_prompt,num_inference_steps,clip_skip"
  scheduler_type: "k_dpmpp_sde"
  

CUDA(NVIDIA) acceleration link

Requirements link

Requirement: nvidia-container-toolkit (installation instructions 1 2)

To check what CUDA version do you need, you can either run nvidia-smi or nvcc --version.

Alternatively, you can also check nvidia-smi with docker:

  docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi
  

To use CUDA, use the images with the cublas tag, for example.

The image list is on quay:

• CUDA 11 tags: master-cublas-cuda11, v1.40.0-cublas-cuda11, …
• CUDA 12 tags: master-cublas-cuda12, v1.40.0-cublas-cuda12, …
• CUDA 11 + FFmpeg tags: master-cublas-cuda11-ffmpeg, v1.40.0-cublas-cuda11-ffmpeg, …
• CUDA 12 + FFmpeg tags: master-cublas-cuda12-ffmpeg, v1.40.0-cublas-cuda12-ffmpeg, …

In addition to the commands to run LocalAI normally, you need to specify --gpus all to docker, for example:

  docker run --rm -ti --gpus all -p 8080:8080 -e DEBUG=true -e MODELS_PATH=/models -e THREADS=1 -v $PWD/models:/models quay.io/go-skynet/local-ai:v1.40.0-cublas-cuda12
  

If the GPU inferencing is working, you should be able to see something like:

  5:22PM DBG Loading model in memory from file: /models/open-llama-7b-q4_0.bin
ggml_init_cublas: found 1 CUDA devices:
  Device 0: Tesla T4
llama.cpp: loading model from /models/open-llama-7b-q4_0.bin
llama_model_load_internal: format     = ggjt v3 (latest)
llama_model_load_internal: n_vocab    = 32000
llama_model_load_internal: n_ctx      = 1024
llama_model_load_internal: n_embd     = 4096
llama_model_load_internal: n_mult     = 256
llama_model_load_internal: n_head     = 32
llama_model_load_internal: n_layer    = 32
llama_model_load_internal: n_rot      = 128
llama_model_load_internal: ftype      = 2 (mostly Q4_0)
llama_model_load_internal: n_ff       = 11008
llama_model_load_internal: n_parts    = 1
llama_model_load_internal: model size = 7B
llama_model_load_internal: ggml ctx size =    0.07 MB
llama_model_load_internal: using CUDA for GPU acceleration
llama_model_load_internal: mem required  = 4321.77 MB (+ 1026.00 MB per state)
llama_model_load_internal: allocating batch_size x 1 MB = 512 MB VRAM for the scratch buffer
llama_model_load_internal: offloading 10 repeating layers to GPU
llama_model_load_internal: offloaded 10/35 layers to GPU
llama_model_load_internal: total VRAM used: 1598 MB
...................................................................................................
llama_init_from_file: kv self size  =  512.00 MB
  

ROCM(AMD) acceleration link

There are a limited number of tested configurations for ROCm systems however most newer deditated GPU consumer grade devices seem to be supported under the current ROCm6 implementation.

Due to the nature of ROCm it is best to run all implementations in containers as this limits the number of packages required for installation on host system, compatability and package versions for dependencies across all variations of OS must be tested independently if disired, please refer to the build documentation.

Requirements link

• ROCm 6.x.x compatible GPU/accelerator
• OS: Ubuntu (22.04, 20.04), RHEL (9.3, 9.2, 8.9, 8.8), SLES (15.5, 15.4)
• Installed to host: amdgpu-dkms and rocm >=6.0.0 as per ROCm documentation.

Recommendations link

• Do not use on a system running Wayland.
• If running with Xorg do not use GPU assigned for compute for desktop rendering.
• Ensure at least 100GB of free space on disk hosting container runtime and storing images prior to installation.

Limitations link

Ongoing verification testing of ROCm compatability with integrated backends. Please note the following list of verified backends and devices.

LocalAI hipblas images are built against the following targets: gfx900,gfx906,gfx908,gfx940,gfx941,gfx942,gfx90a,gfx1030,gfx1031,gfx1100,gfx1101

If your device is not one of these you must specify the corresponding GPU_TARGETS and specify REBUILD=true. Otherwise you don’t need to specify these in the commands below.

Verified link

The devices in the following list have been tested with hipblas images running ROCm 6.0.0

You can help by expanding this list.

System Prep link

1. Check your GPU LLVM target is compatible with the version of ROCm. This can be found in the LLVM Docs.
2. Check which ROCm version is compatible with your LLVM target and your chosen OS (pay special attention to supported kernel versions). See the following for compatability for (ROCm 6.0.0) or (ROCm 6.0.2)
3. Install you chosen version of the dkms and rocm (it is recommended that the native package manager be used for this process for any OS as version changes are executed more easily via this method if updates are required). Take care to restart after installing amdgpu-dkms and before installing rocm, for details regarding this see the installation documentation for your chosen OS (6.0.2 or 6.0.0)
4. Deploy. Yes it’s that easy.

Setup Example (Docker/containerd) link

The following are examples of the ROCm specific configuration elements required.

  # docker-compose.yaml
    # For full functionality select a non-'core' image, version locking the image is recommended for debug purposes.
    image: quay.io/go-skynet/local-ai:master-aio-gpu-hipblas
    environment:
      - DEBUG=true
      # If your gpu is not already included in the current list of default targets the following build details are required.
      - REBUILD=true
      - BUILD_TYPE=hipblas
      - GPU_TARGETS=gfx906 # Example for Radeon VII
    devices:
      # AMD GPU only require the following devices be passed through to the container for offloading to occur.
      - /dev/dri
      - /dev/kfd
  

The same can also be executed as a run for your container runtime

  docker run \
 -e DEBUG=true \
 -e REBUILD=true \
 -e BUILD_TYPE=hipblas \
 -e GPU_TARGETS=gfx906 \
 --device /dev/dri \
 --device /dev/kfd \
 quay.io/go-skynet/local-ai:master-aio-gpu-hipblas
  

Please ensure to add all other required environment variables, port forwardings, etc to your compose file or run command.

The rebuild process will take some time to complete when deploying these containers and it is recommended that you pull the image prior to deployment as depending on the version these images may be ~20GB in size.

Example (k8s) (Advanced Deployment/WIP) link

For k8s deployments there is an additional step required before deployment, this is the deployment of the ROCm/k8s-device-plugin. For any k8s environment the documentation provided by AMD from the ROCm project should be successful. It is recommended that if you use rke2 or OpenShift that you deploy the SUSE or RedHat provided version of this resource to ensure compatability. After this has been completed the helm chart from go-skynet can be configured and deployed mostly un-edited.

The following are details of the changes that should be made to ensure proper function. While these details may be configurable in the values.yaml development of this Helm chart is ongoing and is subject to change.

The following details indicate the final state of the localai deployment relevant to GPU function.

  apiVersion: apps/v1
kind: Deployment
metadata:
  name: {NAME}-local-ai
...
spec:
  ...
  template:
    ...
    spec:
      containers:
        - env:
            - name: HIP_VISIBLE_DEVICES
              value: '0'
              # This variable indicates the devices availible to container (0:device1 1:device2 2:device3) etc.
              # For multiple devices (say device 1 and 3) the value would be equivelant to HIP_VISIBLE_DEVICES="0,2"
              # Please take note of this when an iGPU is present in host system as compatability is not assured.
          ...
          resources:
            limits:
              amd.com/gpu: '1'
            requests:
              amd.com/gpu: '1'
  

This configuration has been tested on a ‘custom’ cluster managed by SUSE Rancher that was deployed on top of Ubuntu 22.04.4, certification of other configuration is ongoing and compatability is not gauranteed.

Notes link

• When installing the ROCM kernel driver on your system ensure that you are installing an equal or newer version that that which is currently implemented in LocalAI (6.0.0 at time of writing).
• AMD documentation indicates that this will ensure functionality however your milage may vary depending on the GPU and distro you are using.
• If you encounter an Error 413 on attempting to upload an audio file or image for whisper or llava/bakllava on a k8s deployment, note that the ingress for your deployment may require the annontation nginx.ingress.kubernetes.io/proxy-body-size: "25m" to allow larger uploads. This may be included in future versions of the helm chart.

Intel acceleration (sycl) link

Requirements link

If building from source, you need to install Intel oneAPI Base Toolkit and have the Intel drivers available in the system.

Container images link

To use SYCL, use the images with the sycl-f16 or sycl-f32 tag, for example v2.23.0-sycl-f32-core, v2.23.0-sycl-f16-ffmpeg-core, …

The image list is on quay.

Example link

To run LocalAI with Docker and sycl starting phi-2, you can use the following command as an example:

  docker run -e DEBUG=true --privileged -ti -v $PWD/models:/build/models -p 8080:8080  -v /dev/dri:/dev/dri --rm quay.io/go-skynet/local-ai:master-sycl-f32-ffmpeg-core phi-2
  

Notes link

In addition to the commands to run LocalAI normally, you need to specify --device /dev/dri to docker, for example:

  docker run --rm -ti --device /dev/dri -p 8080:8080 -e DEBUG=true -e MODELS_PATH=/models -e THREADS=1 -v $PWD/models:/models quay.io/go-skynet/local-ai:v2.23.0-sycl-f16-ffmpeg-core
  

Note also that sycl does have a known issue to hang with mmap: true. You have to disable it in the model configuration if explicitly enabled.