ik_llama.cpp/docker

Build and use ik_llama.cpp with CPU or CPU+CUDA

Built on top of ikawrakow/ik_llama.cpp and llama-swap

All commands are provided for Podman and Docker.

CPU or CUDA sections under Build and Run are enough to get up and running.

Overview

• Build
• Run
• Troubleshooting
• Extra Features
• Credits

Build

Builds two image tags:

• swap: Includes only llama-swap and llama-server.
• full: Includes llama-server, llama-quantize, and other utilities.

Start: download the 4 files to a new directory (e.g. ~/ik_llama/) then follow the next steps.

└── ik_llama
    ├── ik_llama-cpu.Containerfile
    ├── ik_llama-cpu-swap.config.yaml
    ├── ik_llama-cuda.Containerfile
    └── ik_llama-cuda-swap.config.yaml

CPU

podman image build --format Dockerfile --file ik_llama-cpu.Containerfile --target full --tag ik_llama-cpu:full && podman image build --format Dockerfile --file ik_llama-cpu.Containerfile --target swap --tag ik_llama-cpu:swap

docker image build --file ik_llama-cpu.Containerfile --target full --tag ik_llama-cpu:full . && docker image build --file ik_llama-cpu.Containerfile --target swap --tag ik_llama-cpu:swap .

CUDA

podman image build --format Dockerfile --file ik_llama-cuda.Containerfile --target full --tag ik_llama-cuda:full && podman image build --format Dockerfile --file ik_llama-cuda.Containerfile --target swap --tag ik_llama-cuda:swap

docker image build --file ik_llama-cuda.Containerfile --target full --tag ik_llama-cuda:full . && docker image build --file ik_llama-cuda.Containerfile --target swap --tag ik_llama-cuda:swap .

Run

• Download .gguf model files to your favorite directory (e.g. /my_local_files/gguf).
• Map it to /models inside the container.
• Open browser http://localhost:9292 and enjoy the features.
• API endpoints are available at http://localhost:9292/v1 for use in other applications.

CPU

podman run -it --name ik_llama --rm -p 9292:8080 -v /my_local_files/gguf:/models:ro localhost/ik_llama-cpu:swap

docker run -it --name ik_llama --rm -p 9292:8080 -v /my_local_files/gguf:/models:ro ik_llama-cpu:swap

CUDA

• Install Nvidia Drivers and CUDA on the host.
• For Docker, install NVIDIA Container Toolkit
• For Podman, install CDI Container Device Interface

podman run -it --name ik_llama --rm -p 9292:8080 -v /my_local_files/gguf:/models:ro --device nvidia.com/gpu=all --security-opt=label=disable localhost/ik_llama-cuda:swap

docker run  -it --name ik_llama --rm -p 9292:8080 -v /my_local_files/gguf:/models:ro --runtime nvidia ik_llama-cuda:swap

Troubleshooting

• If CUDA is not available, use ik_llama-cpu instead.
• If models are not found, ensure you mount the correct directory: -v /my_local_files/gguf:/models:ro
• If you need to install podman or docker follow the Podman Installation or Install Docker Engine for your OS.

Extra

• CUSTOM_COMMIT can be used to build a specific ik_llama.cpp commit (e.g. 1ec12b8).

podman image build --format Dockerfile --file ik_llama-cpu.Containerfile --target full --build-arg CUSTOM_COMMIT="1ec12b8" --tag ik_llama-cpu-1ec12b8:full && podman image build --format Dockerfile --file ik_llama-cpu.Containerfile --target swap --build-arg CUSTOM_COMMIT="1ec12b8" --tag ik_llama-cpu-1ec12b8:swap

docker image build --file ik_llama-cuda.Containerfile --target full --build-arg CUSTOM_COMMIT="1ec12b8" --tag ik_llama-cuda-1ec12b8:full . && docker image build --file ik_llama-cuda.Containerfile --target swap --build-arg CUSTOM_COMMIT="1ec12b8" --tag ik_llama-cuda-1ec12b8:swap .

• Using the tools in the full image:

$ podman run -it --name ik_llama_full --rm -v /my_local_files/gguf:/models:ro --entrypoint bash localhost/ik_llama-cpu:full
# ./llama-quantize ...
# python3 gguf-py/scripts/gguf_dump.py ...
# ./llama-perplexity ...
# ./llama-sweep-bench ...

docker run  -it --name ik_llama_full --rm -v /my_local_files/gguf:/models:ro --runtime nvidia --entrypoint bash ik_llama-cuda:full
# ./llama-quantize ...
# python3 gguf-py/scripts/gguf_dump.py ...
# ./llama-perplexity ...
# ./llama-sweep-bench ...

• Customize llama-swap config: save the ik_llama-cpu-swap.config.yaml or ik_llama-cuda-swap.config.yaml localy (e.g. under /my_local_files/) then map it to /app/config.yaml inside the container appending -v /my_local_files/ik_llama-cpu-swap.config.yaml:/app/config.yaml:ro to yourpodman run ... or docker run ....
• To run the container in background, replace -it with -d: podman run -d ... or docker run -d .... To stop it: podman stop ik_llama or docker stop ik_llama.
• If you build the image on the same machine where will be used, change -DGGML_NATIVE=OFF to -DGGML_NATIVE=ON in the .Containerfile.
• For a smaller CUDA build, identify your GPU CUDA GPU Compute Capability (e.g. 8.6 for RTX30*0) then change CUDA_DOCKER_ARCH in ik_llama-cuda.Containerfile from default to your GPU architecture (e.g. CUDA_DOCKER_ARCH=86).
• If you build only for your GPU architecture and want to make use of more KV quantization types, build with -DGGML_IQK_FA_ALL_QUANTS=ON.
• Get the best (measures kindly provided on each model card) quants from ubergarm if available.
• Usefull graphs and numbers on @magikRUKKOLA Perplexity vs Size Graphs for the recent quants (GLM-4.7, Kimi-K2-Thinking, Deepseek-V3.1-Terminus, Deepseek-R1, Qwen3-Coder, Kimi-K2, Chimera etc.) topic.
• Build custom quants with Thireus's tools.
• Download from ik_llama.cpp's Thireus fork with release builds for macOS/Windows/Ubuntu CPU and Windows CUDA if you cannot build.
• For a KoboldCPP experience Croco.Cpp is fork of KoboldCPP infering GGML/GGUF models on CPU/Cuda with KoboldAI's UI. It's powered partly by IK_LLama.cpp, and compatible with most of Ikawrakow's quants except Bitnet.

Credits

All credits to the awesome community:

ikawrakow

llama-swap