inference-nv-pytorch 26.01-Container Compute Service(ACS)-阿里云帮助中心

Main features and bug fixes

Main features

Provides images for two CUDA versions: CUDA 12.8 and CUDA 13.0.
- The CUDA 12.8 image supports only the amd64 architecture.
- The CUDA 13.0 image supports both amd64 and aarch64 architectures.
In the CUDA 12.8 image, deepgpu-comfyui is upgraded to 1.4.1, and the deepgpu-torch optimization component to 0.1.18+torch2.9.0cu128.
In both CUDA 12.8 and CUDA 13.0 images, the vLLM version is upgraded to v0.14.0, and the SGLang version to v0.5.7.

Bug fixes

None.

Image name	inference-nv-pytorch
Tag	26.01-vllm0.14.0-pytorch2.9-cu128-20260121-serverless	26.01-sglang0.5.7-pytorch2.9-cu128-20260113-serverless	26.01-vllm0.14.0-pytorch2.9-cu130-20260123-serverless		26.01-sglang0.5.7-pytorch2.9-cu130-20260113-serverless
Supported architecture	amd64	amd64	amd64	aarch64	amd64	aarch64
Application scenario	large model inference	large model inference	large model inference	large model inference	large model inference	large model inference
Framework	pytorch	pytorch	pytorch	pytorch	pytorch	pytorch
Requirements	NVIDIA Driver release >= 570	NVIDIA Driver release >= 570	NVIDIA Driver release >= 580	NVIDIA Driver release >= 580	NVIDIA Driver release >= 580	NVIDIA Driver release >= 580
System components	Ubuntu 24.04 Python 3.12 Torch 2.9.1 CUDA 12.8 diffusers 0.36.0 deepgpu-comfyui 1.4.1 deepgpu-torch 0.1.18+torch2.9.0cu128 flash_attn 2.8.3 flashinfer-python 0.5.3 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.53.0 transformers 4.57.6 triton 3.5.1 torchaudio 2.9.1 torchvision 0.24.1 vllm 0.14.0 xfuser 0.4.5 xgrammar 0.1.27 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.9.1+cu128 CUDA 12.8 torchaudio 2.9.1+128 torchvision 0.24.1+128 diffusers 0.36.0 decord 0.6.0 decord2 3.0.0 deepgpu-comfyui 1.4.1 deepgpu-torch 0.1.18+torch2.9.0cu128 flash_attn 2.8.3 flash_mla 1.0.0+1408756 flashinfer-python 0.5.3 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.53.0 transformers 4.57.1 sgl-kernel 0.3.20 sglang 0.5.7 xgrammar 0.1.27 triton 3.5.1 torchao 0.9.0 xfuser 0.4.5 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.9.1+cu130 CUDA 13.0.2 diffusers 0.36.0 flash_attn 2.8.3 flashinfer-python 0.5.3 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.53.1 transformers 4.57.6 triton 3.5.0 torchaudio 2.9.1+cu130 torchvision 0.24.1+cu130 vllm 0.14.0 xfuser 0.4.5 xgrammar 0.1.27 ljperf 0.1.0+d0e4a408	Ubuntu 24.04 Python 3.12 Torch 2.9.1+cu130 CUDA 13.0.2 diffusers 0.36.0 flash_attn 2.8.3 flashinfer-python 0.5.3 transformers 4.57.6 ray 2.53.0 vllm 0.14.0 triton 3.5.1 torchaudio 2.9.1+cu130 torchvision 0.24.1+cu130 xfuser 0.4.5 xgrammar 0.1.27 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.9.1+cu130 CUDA 13.0.2 diffusers 0.36.0 decord 0.6.0 decord2 3.0.0 flash_attn 2.8.3 flashinfer-python 0.5.3 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.53.0 transformers 4.57.1 sgl-kernel 0.3.20 sglang 0.5.7 xgrammar 0.1.27 triton 3.5.1 torchao 0.9.0 torchaudio 2.9.1 torchvision 0.24.1+cu130 xfuser 0.4.5 ljperf 0.1.0+d0e4a408	Ubuntu 24.04 Python 3.12 Torch 2.9.1+cu130 CUDA 13.0.2 diffusers 0.36.0 decord2 3.0.0 flash_attn 2.8.3 flashinfer-python 0.5.3 imageio 2.37.2 imageio-ffmpeg 0.6.0 transformers 4.57.1 sgl-kernel 0.3.20 sglang 0.5.7 xgrammar 0.1.27 triton 3.5.1 torchao 0.9.0 torchaudio 2.9.1 torchvision 0.24.1 xfuser 0.4.5

Assets

Public images

CUDA 12.8 assets

egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:26.01-vllm0.14.0-pytorch2.9-cu128-20260121-serverless
egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:26.01-sglang0.5.7-pytorch2.9-cu128-20260113-serverless

CUDA 13.0 assets

egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:26.01-vllm0.14.0-pytorch2.9-cu130-20260123-serverless
egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:26.01-sglang0.5.7-pytorch2.9-cu130-20260113-serverless

VPC images

To pull ACS AI container images quickly within a VPC, replace the public AI container image asset URI egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/{image:tag} with acs-registry-vpc.{region-id}.cr.aliyuncs.com/egslingjun/{image:tag}.

{region-id}: The region ID of an ACS available region. For example: cn-beijing or cn-wulanchabu.
{image:tag}: The name and tag of the AI container image. For example: inference-nv-pytorch:25.10-vllm0.11.0-pytorch2.8-cu128-20251028-serverless or training-nv-pytorch:25.10-serverless.

Note

These images support the ACS and Lingjun multi-tenant product formats. They are not compatible with the Lingjun single-tenant product format. Do not use these images in a Lingjun single-tenant environment.

Driver requirements

CUDA 12.8: NVIDIA Driver release >= 570
CUDA 13.0: NVIDIA Driver release >= 580

Quick start

The following example shows how to pull the inference-nv-pytorch image with Docker and test the inference service with the Qwen2.5-7B-Instruct model.

Note

To use the inference-nv-pytorch image in ACS, select it from the Artifact Center page when you create a workload in the console, or specify the image reference in a YAML file. For more information, see the following topics about building a model inference service by using ACS GPU compute power:

Pull the inference container image.

docker pull egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:[tag]

Download the open-source model from ModelScope.

pip install modelscope
cd /mnt
modelscope download --model Qwen/Qwen2.5-7B-Instruct --local_dir ./Qwen2.5-7B-Instruct

Run the following command to enter the container.

docker run -it --rm --gpus all --network=host --privileged --init --ipc=host \
--ulimit memlock=-1 --ulimit stack=67108864  \
-v /mnt/:/mnt/ \
egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:[tag]

Run an inference test on the vLLM chat feature.

Start the server-side service.

python3 -m vllm.entrypoints.openai.api_server \
--model /mnt/Qwen2.5-7B-Instruct \
--trust-remote-code --disable-custom-all-reduce \
--tensor-parallel-size 1

Run the test on the client.

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "/mnt/Qwen2.5-7B-Instruct",  
    "messages": [
    {"role": "system", "content": "You are a friendly AI assistant."},
    {"role": "user", "content": "Introduce deep learning."}
    ]}'

Output:

{"id":"chat-d3c28759793d4376a65bfc4e40b59a71","object":"chat.completion","created":1735278194,"model":"/mnt/deep_learning_test/testsuite/dataset/llms_inference_qwen7b-v2.5_accelerate/checkpoint/7B-V2.5/","choices":[{"index":0,"message":{"role":"assistant","content":"Deep learning is a branch of machine learning inspired by biological nervous systems, especially the interaction between neurons in the brain. Deep learning uses deep neural networks to process and analyze large amounts of data to identify effective predictive models. This technology has achieved significant success in many fields, such as image recognition, speech processing, and natural language processing.\n\nIn deep learning, a neural network consists of multiple layers, including an input layer, several hidden layers, and an output layer. Each layer contains multiple nodes (or neurons) that are connected to nodes in other layers through weighted connections. During the training process, the neural network adjusts the weights of these connections based on the input data to minimize the error between the predicted output and the actual labels. This process is typically implemented using optimization algorithms such as gradient descent.\n\nTraining deep learning models requires substantial computing resources and data support. In recent years, with advancements in computing hardware (such as GPUs and TPUs) and the rapid growth of datasets, deep learning technology has been widely applied and developed. In addition to the applications mentioned above, deep learning is also widely used in medical diagnosis, autonomous driving, gaming, and finance.","tool_calls":[]},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":237,"completion_tokens":213}}

For more information about how to use vLLM, see the vLLM documentation.

Known issues

The deepgpu-comfyui plugin for accelerating Wan model video generation currently supports only the GN8IS, G49E, and G59 instance types.