inference-nv-pytorch 26.02 - Container Compute Service - Alibaba Cloud Documentation Center

Features and fixes

Features

This release 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.
For the vLLM image, Torch is upgraded to 2.10.0, and vLLM is upgraded to v0.15.1.
For the SGLang image, Torch is upgraded to 2.10.0, and SGLang is upgraded to v0.5.9.

Bug fixes

No bug fixes in this release.

Image name	inference-nv-pytorch
Tag	26.02-vllm0.15.1-pytorch2.10-cu128-20260211-serverless	26.02-sglang0.5.9-pytorch2.10-cu128-20260227-serverless	26.02-vllm0.15.1-pytorch2.10-cu130-20260211-serverless		26.02-sglang0.5.9-pytorch2.10-cu130-20260227-serverless
Supported architecture	amd64	amd64	amd64	aarch64	amd64	aarch64
Use case	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 2.10.0 CUDA 12.8 NCCL 2.28.9 diffusers 0.36.0 flash_attn 2.8.3 flash_attn_3 3.0.0 flashinfer-python 0.6.1 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.53.0 transformers 4.57.6 triton 3.6.0 torchaudio 2.10.0 torchvision 0.25.0 vllm 0.15.1 xfuser 0.4.5 xgrammar 0.1.27 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.10.0 CUDA 12.8 NCCL 2.28.9 torchaudio 2.10.0 torchvision 0.25.0 diffusers 0.36.0 decord 0.6.0 decord2 3.0.0 flash_attn 2.8.3 flash_attn_3 3.0.0 flashinfer-python 0.6.3 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.54.0 transformers 4.57.1 sgl-kernel 0.3.21 sglang 0.5.9 xgrammar 0.1.27 triton 3.6.0 torchao 0.9.0 xfuser 0.4.5 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.10.0+cu130 CUDA 13.0.2 NCCL 2.28.9 diffusers 0.36.0 flash_attn 2.8.3 flash_attn_3 3.0.0 flashinfer-python 0.6.1 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.53.0 transformers 4.57.6 triton 3.6.0 torchaudio 2.10.0+cu130 torchvision 0.25.0+cu130 vllm 0.15.1 xfuser 0.4.5 xgrammar 0.1.27 ljperf 0.1.0+d0e4a408	Ubuntu 24.04 Python 3.12 Torch 2.10.0+cu130 CUDA 13.0.2 NCCL 2.28.9 diffusers 0.36.0 flash_attn 2.8.3 flashinfer-python 0.6.1 transformers 4.57.6 ray 2.53.0 vllm 0.15.1 triton 3.6.0 torchaudio 2.10.0+cu130 torchvision 0.25.0+cu130 xfuser 0.4.5 xgrammar 0.1.27 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.10.0+cu130 CUDA 13.0.2 NCCL 2.28.9 diffusers 0.36.0 decord 0.6.0 decord2 3.0.0 flash_attn 2.8.3 flashinfer-python 0.6.3 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.54.0 transformers 4.57.1 sgl-kernel 0.3.21 sglang 0.5.9 xgrammar 0.1.27 triton 3.6.0 torchao 0.9.0 torchaudio 2.10.0+cu130 torchvision 0.25.0+cu130 xfuser 0.4.5 ljperf 0.1.0+d0e4a408	Ubuntu 24.04 Python 3.12 Torch 2.10.0+cu130 CUDA 13.0.2 NCCL 2.28.9 diffusers 0.36.0 decord2 3.0.0 flash_attn 2.8.3 flashinfer-python 0.6.3 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.54.0 transformers 4.57.1 sgl-kernel 0.3.21 sglang 0.5.9 xgrammar 0.1.27 triton 3.6.0 torchao 0.9.0 torchaudio 2.10.0+cu130 torchvision 0.25.0+cu130 xfuser 0.4.5 ljperf 0.1.0+477686c5

Assets

Public image

CUDA 12.8 assets

egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:26.02-vllm0.15.1-pytorch2.10-cu128-20260211-serverless
egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:26.02-sglang0.5.9-pytorch2.10-cu128-20260227-serverless

CUDA 13.0 assets

egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:26.02-vllm0.15.1-pytorch2.10-cu130-20260211-serverless
egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:26.02-sglang0.5.9-pytorch2.10-cu130-20260227-serverless

VPC image

For faster ACS AI container image pulls from a VPC, replace the public image 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 ID of the ACS region. Examples: cn-beijing and cn-wulanchabu.
{image:tag}: The name and tag of the AI container image. Examples: inference-nv-pytorch:25.10-vllm0.11.0-pytorch2.8-cu128-20251028-serverless and training-nv-pytorch:25.10-serverless.

Note

This image is for ACS and Lingjun multi-tenant products. It is not supported in Lingjun single-tenant scenarios.

Driver requirements

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

Quick start

The following example demonstrates 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 the image on the Artifacts 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 how to build a model inference service with ACS GPU compute:

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 start and 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]

Test the vLLM conversational 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 a test on the client-side.

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 helpful AI assistant."},
    {"role": "user", "content": "Introduce deep learning."}
    ]}'

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

Known issues

The images in this release do not support the deepgpu-comfyui plug-in.

Container Compute Service:inference-nv-pytorch 26.02