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https://github.com/scitrera/cuda-containers

Scitrera builds of various CUDA containers for version consistency, starting primarily with NVIDIA DGX Spark Containers
https://github.com/scitrera/cuda-containers

Last synced: 2 months ago
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Scitrera builds of various CUDA containers for version consistency, starting primarily with NVIDIA DGX Spark Containers

Awesome Lists containing this project

README 

          
# CUDA Containers for NVIDIA DGX Spark



https://github.com/scitrera/cuda-containers



This repository contains Dockerfiles and build recipes for CUDA-based containers optimized for **NVIDIA DGX Spark**

systems, with a focus on **vLLM**, **sglang**, **llama.cpp**, **PyTorch**, and multi-node inference workloads.



The primary goal of this project is to provide **stable, well-versioned, prebuilt images** that work out-of-the-box on

DGX Spark (Blackwell-ready), while still being suitable as **base images** for custom builds.



---



## Why This Repo Exists



The official NVIDIA images tend to run too far behind the latest releases. Other community images prioritize bleeding

edge over versioning and stability.



The goal of this repo is to provide a **stable, well-versioned, prebuilt images** that work out-of-the-box on DGX

Spark (Blackwell-ready).



The main architectural difference from other builds (e.g. eugr's repo (link below) -- which is pretty much the community

standard) is:



- **NCCL and PyTorch are built first**, in a dedicated base image

- vLLM and related tooling are layered on top

- Versioning follows **vLLM releases** as the primary axis



If you need the *absolute latest vLLM features from git right now*, I still strongly recommend:

https://github.com/eugr/spark-vllm-docker



For sglang, the officially provided container is not continuously updated. I assume that might change

in the near future as sglang gets better SM121 support -- but in the meantime, Scitrera will, on a best effort basis,

maintain sglang images similar to our vLLM images.



---



## Available Images



### vLLM Images



All vLLM images:



- Are optimized for DGX Spark

- Include **Ray** for multi-node / cluster deployments

- Rebuild PyTorch, Triton, and vLLM against updated NCCL

- Support tensor parallelism (`-tp`) and multi-node inference

- are hosted on Docker

  Hub: [https://hub.docker.com/r/scitrera/dgx-spark-vllm](https://hub.docker.com/r/scitrera/dgx-spark-vllm)



#### Latest Releases



##### vLLM 0.16.0



- `scitrera/dgx-spark-vllm:0.16.0-t4`

    - vLLM 0.16.0

    - PyTorch 2.10.0 (with torchvision + torchaudio)

    - CUDA 13.1.1

    - Transformers 4.57.6

    - Triton 3.6.0

    - NCCL 2.29.3-1

    - FlashInfer 0.6.3



- `scitrera/dgx-spark-vllm:0.16.0-t5`

    - Same as above, but with **Transformers 5.2.0**



##### vLLM 0.15.1



- `scitrera/dgx-spark-vllm:0.15.1-t4`

    - vLLM 0.15.1

    - PyTorch 2.10.0 (with torchvision + torchaudio)

    - CUDA 13.1.0

    - Transformers 4.57.6

    - Triton 3.5.1 *(3.6.0 not yet compatible)*

    - NCCL 2.29.2-1

    - FlashInfer 0.6.2



- `scitrera/dgx-spark-vllm:0.15.1-t5`

    - Same as above, but with **Transformers 5.0.0**



##### Earlier Builds



- `scitrera/dgx-spark-vllm:0.15.0-t4`

- `scitrera/dgx-spark-vllm:0.15.0-t5`

- `scitrera/dgx-spark-vllm:0.14.1-t4`

- `scitrera/dgx-spark-vllm:0.14.1-t5`

- `scitrera/dgx-spark-vllm:0.14.0-t4`

- `scitrera/dgx-spark-vllm:0.14.0-t5`

    - Includes a patch to `is_deepseek_mla()` for **GLM-4.7-Flash**

    - Tested successfully with Ray and `-tp4` on a 4-node DGX Spark cluster



- `scitrera/dgx-spark-vllm:0.13.0-t4`



---



### SGLang Images



SGLang images are also optimized for DGX Spark and provide an alternative high-performance inference runtime.



- are hosted on Docker

  Hub: [https://hub.docker.com/r/scitrera/dgx-spark-sglang](https://hub.docker.com/r/scitrera/dgx-spark-sglang)



#### Latest Releases



##### SGLang 0.5.8



- `scitrera/dgx-spark-sglang:0.5.8-t4`

    - SGLang 0.5.8 (with build fixes post-release)

    - PyTorch 2.10.0 (with torchvision + torchaudio)

    - CUDA 13.1.1

    - Transformers 4.57.6

    - Triton 3.6.0

    - NCCL 2.29.3-1

    - FlashInfer 0.6.3



- `scitrera/dgx-spark-sglang:0.5.8-t5`

    - Same as above, but with **Transformers 5.2.0**



---



### llama.cpp Images



llama.cpp images provide a lightweight, self-contained C++ inference runtime for GGUF models on DGX Spark — no

Python or PyTorch required. Built directly from source with CUDA support.



- Are hosted on Docker

  Hub: [https://hub.docker.com/r/scitrera/dgx-spark-llama-cpp](https://hub.docker.com/r/scitrera/dgx-spark-llama-cpp)



#### Latest Releases



##### llama.cpp b8076



- `scitrera/dgx-spark-llama-cpp:b8076-cu131`

    - llama.cpp build 8076

    - CUDA 13.1.1

    - Built on `nvidia/cuda:13.1.1-devel-ubuntu24.04`

    - Includes llama-server, llama-cli, llama-quantize, and all standard tools

    - GGML CUDA and RPC backends enabled



---



### PyTorch Development Base Image



If you want to build your own inference stack:



- **`scitrera/dgx-spark-pytorch-dev:2.10.0-v2-cu131`**

    - PyTorch 2.10.0

    - CUDA 13.1.1

    - NCCL 2.29.3-1

    - Built on `nvidia/cuda:13.1.1-devel-ubuntu24.04`

    - Includes standard build tooling



- **`scitrera/dgx-spark-pytorch-dev:2.10.0-cu131`**

    - PyTorch 2.10.0

    - CUDA 13.1.0

    - NCCL 2.29.2-1

    - Built on `nvidia/cuda:13.1.0-devel-ubuntu24.04`

    - Includes standard build tooling



This is the recommended base image if you want to:



- Build vLLM/sglang/other tools yourself

- Add custom kernels or extensions

- Experiment with alternative runtimes



---



## Tag Semantics



Tags follow this pattern for vLLM and SGLang containers:



```

-t

```



Examples:



- `0.13.0-t4` → vLLM 0.13.0 + Transformers 4.x

- `0.5.8-t5` → SGLang 0.5.8 + Transformers 5.x



For llama.cpp containers:



```

b-cu

```



Examples:



- `b8076-cu131` → llama.cpp build 8076 + CUDA 13.1.1



---



## Example Usage (vLLM)



```bash

docker run \

  --privileged \

  --gpus all \

  -it --rm \

  --network host --ipc=host \

  -v ~/.cache/huggingface:/root/.cache/huggingface \

  scitrera/dgx-spark-vllm:0.16.0-t4 \

  vllm serve \

    Qwen/Qwen2.5-7B-Instruct \

    --gpu-memory-utilization 0.4

````



---



## Example Usage (SGLang)



```bash

docker run \

  --privileged \

  --gpus all \

  -it --rm \

  --network host --ipc=host \

  -v ~/.cache/huggingface:/root/.cache/huggingface \

  scitrera/dgx-spark-sglang:0.5.8-t4 \

  sglang serve \

    --model-path Qwen/Qwen2.5-7B-Instruct \

    --mem-fraction-static 0.4

````



---



## Example Usage (llama.cpp)



```bash

docker run \

  --privileged \

  --gpus all \

  -it --rm \

  --network host --ipc=host \

  -v ~/models:/models \

  scitrera/dgx-spark-llama-cpp:b8076-cu131 \

  --model /models/my-model.gguf \

  --host 0.0.0.0 --port 8080

```



To use the CLI instead of the server:



```bash

docker run \

  --privileged \

  --gpus all \

  -it --rm \

  --entrypoint llama-cli \

  -v ~/models:/models \

  scitrera/dgx-spark-llama-cpp:b8076-cu131 \

  -m /models/my-model.gguf \

  -p "Hello, world!" -n 128

```



---



## Inspecting Component Versions



Major component versions are embedded as Docker labels.



```bash

docker inspect scitrera/dgx-spark-vllm:0.14.0rc2-t4 \

  --format '{{json .Config.Labels}}' | jq

```



Example output:



```json

{

  "dev.scitrera.cuda_version": "13.1.0",

  "dev.scitrera.flashinfer_version": "0.6.1",

  "dev.scitrera.nccl_version": "2.28.9-1",

  "dev.scitrera.torch_version": "2.10.0-rc6",

  "dev.scitrera.transformers_version": "4.57.5",

  "dev.scitrera.triton_version": "3.5.1",

  "dev.scitrera.vllm_version": "0.14.0rc2"

}

```



---



## Notes & Caveats



* NCCL is upgraded relative to upstream PyTorch builds

* PyTorch, Triton, and vLLM/sglang are rebuilt accordingly

* Image sizes could still be optimized further

* Version combinations are chosen to be as new as possible but limited by **stability** (not guaranteed to have the

  latest features if they might break things)



---



## Roadmap (Loose)



* Better size optimization

* More documentation/support for DGX Spark newcomers



---



## Acknowledgements



This work is inspired by and complementary to:



* @eugr’s DGX Spark vLLM images

  [https://github.com/eugr/spark-vllm-docker](https://github.com/eugr/spark-vllm-docker)

* Everyone else who contributed to the NVIDIA DGX spark forums, especially in the first two months after the DGX Spark's

  release. Getting things to work was really a mess!



This project is not affiliated with NVIDIA. This project is sponsored and maintained

by [scitrera.ai](https://scitrera.ai/).



If you need the very latest vLLM feature added four hours ago, start with eugr's repo.



If you want stable, prebuilt images with predictable versioning, use the docker images built from this repo.