vLLM Ray Cluster Node Docker for DGX Spark

This repository contains the Docker configuration and startup scripts to run a multi-node vLLM inference cluster using Ray. It supports InfiniBand/RDMA (NCCL) and custom environment configuration for high-performance setups.

DISCLAIMER

This repository is not affiliated with NVIDIA or their subsidiaries. The content is provided as a reference material only, not intended for production use. Some of the steps and parameters may be unnecessary, and some may be missing. This is a work in progress. Use at your own risk!

The Dockerfile builds from the main branch of VLLM, so depending on when you run the build process, it may not be in fully functioning state.

CHANGELOG

2025-12-18

Updated build-and-copy.sh to support copying to multiple hosts.

• Added -c, --copy-to (accepts space- or comma-separated host lists) and kept --copy-to-host as a backward-compatible alias.
• Added --copy-parallel to copy to all hosts concurrently.
• Short -h is now used for help.

2025-12-15

Updated build-and-copy.sh flags:

• Renamed --triton-sha to --triton-ref to support branches and tags in addition to commit SHAs.
• Added --vllm-ref <ref>: Specify vLLM commit SHA, branch or tag (defaults to main).

2025-12-14

Converted to multi-stage Docker build with improved build times and reduced final image size. The builder stage is now separate from the runtime stage, excluding unnecessary build tools from the final image.

Added timing statistics to build-and-copy.sh to track Docker build and image copy durations, displaying a summary at the end.

Triton is now being built from the source, alongside with its companion triton_kernels package. The Triton version is set to v3.5.1 by default, but it can be changed by using --triton-sha parameter.

Added new flags to build-and-copy.sh:

• --triton-sha <sha>: Specify Triton commit SHA (defaults to v3.5.1 currently)
• --no-build: Skip building and only copy existing image (requires --copy-to)

2025-12-11 update

PR for MiniMax-M2 has been merged into main, so removed the temporary patch from Dockerfile.

2025-12-11

Applied a patch to fix broken MiniMax-M2 in some quants after this commit until this PR is approved. See this issue for details.

2025-12-05

Added build-and-copy.sh for convenience.

2025-11-26

Initial release. Updated RoCE configuration example to include both interfaces in the list. Applied patch to enable FastSafeTensors in cluster configuration (EXPERIMENTAL) and added documentation on fastsafetensors use.

1. Building the Docker Image

Building Manually

The Dockerfile includes specific Build Arguments to allow you to selectively rebuild layers (e.g., update the vLLM source code without re-downloading PyTorch). Using a provided build script is recommended, but if you want to build using docker build command, here are the supported build arguments:

Argument	Default	Description
CACHEBUST_DEPS	1	Change this to force a re-download of PyTorch, FlashInfer, and system dependencies.
CACHEBUST_VLLM	1	Change this to force a fresh git clone and rebuild of vLLM source code.
TRITON_REF	v3.5.1	Triton commit SHA, branch, or tag to build.
VLLM_REF	main	vLLM commit SHA, branch, or tag to build.

Using the Build Script (Recommended)

The build-and-copy.sh script automates the build process and optionally copies the image to one or more nodes. This is the recommended method for building and deploying to multiple Spark nodes.

Basic usage (build only):

./build-and-copy.sh

Build with a custom tag:

./build-and-copy.sh --tag my-vllm-node

Build and copy to Spark node(s):

Using the same username as currently logged-in user (single host):

./build-and-copy.sh --copy-to 192.168.177.12

Copy to multiple hosts (space- or comma-separated after the flag):

./build-and-copy.sh --copy-to 192.168.177.12 192.168.177.13

Copy to multiple hosts in parallel:

./build-and-copy.sh --copy-to 192.168.177.12 192.168.177.13 --copy-parallel

Using a different username:

./build-and-copy.sh --copy-to 192.168.177.12 --user your_username

Force rebuild vLLM source only:

./build-and-copy.sh --rebuild-vllm

Force rebuild all dependencies:

./build-and-copy.sh --rebuild-deps

Combined example (rebuild vLLM and copy to another node):

./build-and-copy.sh --rebuild-vllm --copy-to 192.168.177.12

Build with specific Triton commit:

./build-and-copy.sh --triton-ref abc123def456

Copy existing image without rebuilding:

./build-and-copy.sh --no-build --copy-to 192.168.177.12

Available options:

Flag	Description
-t, --tag <tag>	Image tag (default: 'vllm-node')
--rebuild-deps	Force rebuild all dependencies (sets CACHEBUST_DEPS)
--rebuild-vllm	Force rebuild vLLM source only (sets CACHEBUST_VLLM)
--triton-ref <ref>	Triton commit SHA, branch or tag (default: 'v3.5.1')
--vllm-ref <ref>	vLLM commit SHA, branch or tag (default: 'main')
-c, --copy-to <host[,host...] or host host...>	Host(s) to copy the image to after building (space- or comma-separated list after the flag).
--copy-to-host	Alias for --copy-to (backwards compatibility).
--copy-parallel	Copy to all specified hosts concurrently.
-u, --user <user>	Username for SSH connection (default: current user)
--no-build	Skip building, only copy existing image (requires --copy-to)
-h, --help	Show help message

IMPORTANT: When copying to another node, make sure you use the Spark IP assigned to its ConnectX 7 interface (enp1s0f1np1), and not the 10G interface (enP7s7)!

Copying the container to another Spark node (Manual Method)

Alternatively, you can manually copy the image directly to your second Spark node via ConnectX 7 interface by using the following command:

docker save vllm-node | ssh your_username@another_spark_hostname_or_ip "docker load"

IMPORTANT: make sure you use Spark IP assigned to it's ConnectX 7 interface (enp1s0f1np1) , and not 10G one (enP7s7)!

---

2. Running the Container

Ray and NCCL require specific Docker flags to function correctly across multiple nodes (Shared memory, Network namespace, and Hardware access).

docker run -it --rm \
  --gpus all \
  --net=host \
  --ipc=host \
  --privileged \
  --name vllm_node \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm-node bash

Or if you want to start the cluster node (head or regular), you can launch with the run-cluster.sh script (see details below):

On head node:

docker run --privileged --gpus all -it --rm \
  --ipc=host \
  --network host \
  --name vllm_node \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm-node ./run-cluster-node.sh \
    --role head \
    --host-ip 192.168.177.11 \
    --eth-if enp1s0f1np1 \
    --ib-if rocep1s0f1,roceP2p1s0f1 

On worker node

docker run --privileged --gpus all -it --rm \
  --ipc=host \
  --network host \
  --name vllm_node \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm-node ./run-cluster-node.sh \
    --role node \
    --host-ip 192.168.177.12 \
    --eth-if enp1s0f1np1 \
    --ib-if rocep1s0f1,roceP2p1s0f1 \
    --head-ip 192.168.177.11

IMPORTANT: use the IP addresses associated with ConnectX 7 interface, not with 10G or wireless one!

Flags Explained:

• --net=host: Required. Ray and NCCL need full access to host network interfaces.
• --ipc=host: Recommended. Allows shared memory access for PyTorch/NCCL. As an alternative, you can set it via --shm-size=16g.
• --privileged: Recommended for InfiniBand. Grants the container access to RDMA devices (/dev/infiniband). As an alternative, you can pass --ulimit memlock=-1 --ulimit stack=67108864 --device=/dev/infiniband.

---

3. Using run-cluster-node.sh

The script is used to configure the environment and launch Ray either in head or node mode.

Normally you would start it with the container like in the example above, but you can launch it inside the Docker session manually if needed (but make sure it's not already running).

Syntax

./run-cluster-node.sh [OPTIONS]

Flag	Long Flag	Description	Required?
-r	--role	Role of the machine: head or node.	Yes
-h	--host-ip	The IP address of this specific machine (for ConnectX port, e.g. enp1s0f1np1).	Yes
-e	--eth-if	ConnectX 7 Ethernet interface name (e.g., enp1s0f1np1).	Yes
-i	--ib-if	ConnectX 7 InfiniBand interface name (e.g., rocep1s0f1 - on Spark specifically you want to use both "twins": rocep1s0f1,roceP2p1s0f1).	Yes
-m	--head-ip	The IP address of the Head Node.	Only if role is node

Hint: to decide which interfaces to use, you can run ibdev2netdev. You will see an output like this:

rocep1s0f0 port 1 ==> enp1s0f0np0 (Down)
rocep1s0f1 port 1 ==> enp1s0f1np1 (Up)
roceP2p1s0f0 port 1 ==> enP2p1s0f0np0 (Down)
roceP2p1s0f1 port 1 ==> enP2p1s0f1np1 (Up)

Each physical port on Spark has two pairs of logical interfaces in Linux. Current NVIDIA guidance recommends using only one of them, in this case it would be enp1s0f1np1 for Ethernet, but use both rocep1s0f1,roceP2p1s0f1 for IB.

You need to make sure you allocate IP addresses to them (no need to allocate IP to their "twins").

Example: Starting inside the Head Node

./run-cluster-node.sh \
  --role head \
  --host-ip 192.168.177.11 \
  --eth-if enp1s0f1np1 \
  --ib-if rocep1s0f1,roceP2p1s0f1

Example: Starting inside a Worker Node

./run-cluster-node.sh \
  --role node \
  --host-ip 192.168.177.12 \
  --eth-if enp1s0f1np1 \
  --ib-if rocep1s0f1,roceP2p1s0f1 \
  --head-ip 192.168.177.11

---

4. Configuration Details

Environment Persistence

The script automatically appends exported variables to ~/.bashrc. If you need to open a second terminal into the running container for debugging, simply run:

docker exec -it vllm_node bash

All environment variables (NCCL, Ray, vLLM config) set by the startup script will be loaded automatically in this new session.

5. Using cluster mode for inference

First, start follow the instructions above to start the head container on your first Spark, and node container on the second Spark. Then, on the first Spark, run vllm like this:

docker exec -it vllm_node bash -i -c "vllm serve RedHatAI/Qwen3-VL-235B-A22B-Instruct-NVFP4 --port 8888 --host 0.0.0.0 --gpu-memory-utilization 0.7 -tp 2 --distributed-executor-backend ray --max-model-len 32768"

Alternatively, run an interactive shell first:

docker exec -it vllm_node

And execute vllm command inside.

6. Fastsafetensors

This build includes support for fastsafetensors loading which significantly improves loading speeds, especially on DGX Spark where MMAP performance is very poor currently. Fasttensors solve this issue by using more efficient multi-threaded loading while avoiding mmap.

This build also implements an EXPERIMENTAL patch to allow use of fastsafetensors in a cluster configuration (it won't work without it!). Please refer to this issue for the details.

To use this method, simply include --load-format fastsafetensors when running VLLM, for example:

HF_HUB_OFFLINE=1 vllm serve openai/gpt-oss-120b --port 8888 --host 0.0.0.0 --trust_remote_code --swap-space 16 --gpu-memory-utilization 0.7 -tp 2 --distributed-executor-backend ray --load-format fastsafetensors

7. Benchmarking

Follow the guidance in VLLM Benchmark Suites to download benchmarking dataset, and then run a benchmark with a command like this (assuming you are running on head node, otherwise specify --host parameter):

vllm bench serve \
  --backend vllm \
  --model RedHatAI/Qwen3-VL-235B-A22B-Instruct-NVFP4 \
  --endpoint /v1/completions   --dataset-name sharegpt \
  --dataset-path ShareGPT_V3_unfiltered_cleaned_split.json \
  --num-prompts 1 \
  --port 8888 

Modify --num-prompts to benchmark concurrent requests - the command above will give you single request performance.

Hardware Architecture

Note: The Dockerfile defaults to TORCH_CUDA_ARCH_LIST=12.1a (NVIDIA GB10). If you are using different hardware, update the ENV variable in the Dockerfile before building.