inference-nv-pytorch 25.11 - Container Compute Service - Alibaba Cloud Documentation Center

This document provides the release notes for inference-nv-pytorch 25.11.

Key features and bug fixes

Key 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.
Upgraded PyTorch to 2.9.0.
Upgraded deepgpu-comfyui to 1.3.2 and the deepgpu-torch optimization component to 0.1.12+torch2.9.0cu128 in the CUDA 12.8 image.
Upgraded vLLM to v0.11.2 and SGLang to v0.5.5.post3 in the CUDA 12.8 and CUDA 13.0 images.

Bug fixes

None

Image name	inference-nv-pytorch
Image tag	25.11-vllm0.11.1-pytorch2.9-cu128-20251120-serverless	25.11-sglang0.5.5.post3-pytorch2.9-cu128-20251121-serverless	25.11-vllm0.11.1-pytorch2.9-cu130-20251120-serverless		25.11-sglang0.5.5.post3-pytorch2.9-cu130-20251121-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 2.9.0+cu128 CUDA 12.8 diffusers 0.35.2 deepgpu-comfyui 1.3.2 deepgpu-torch 0.1.12+torch2.9.0cu128 flash_attn 2.8.3 flashinfer-python 0.5.2 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.51.1 transformers 4.57.1 triton 3.4.0 torchaudio 2.8.0+cu128 torchvision 0.24.0+cu128 vllm 0.11.1 xfuser 0.4.5 xgrammar 0.1.25 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.9.0+cu128 CUDA 12.8 diffusers 0.35.2 decord 0.6.0 decord2 2.0.0 deepgpu-comfyui 1.3.2 deepgpu-torch 0.1.12+torch2.9.0cu128 flash_attn 2.8.3 flash_mla 1.0.0+1408756 flashinfer-python 0.5.2 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.51.1 transformers 4.57.1 sgl-kernel 0.3.17.post1 sglang 0.5.5.post3 xgrammar 0.1.25 triton 3.5.0 torchao 0.9.0 torchaudio 2.8.0+cu128 torchvision 0.24.0+cu128 xfuser 0.4.5 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.9.0+cu130 CUDA 13.0.2 diffusers 0.35.2 flash_attn 2.8.3 flashinfer-python 0.5.2 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.51.1 transformers 4.57.1 triton 3.5.0 torchaudio 2.9.0+cu130 torchvision 0.24.0+cu130 vllm 0.11.2 xfuser 0.4.5 xgrammar 0.1.25 ljperf 0.1.0+d0e4a408	Ubuntu 24.04 Python 3.12 Torch 2.9.0+cu130 CUDA 13.0.2 diffusers 0.35.2 flash_attn 2.8.3 flashinfer-python 0.5.2 transformers 4.57.1 ray 2.51.1 vllm 0.11.1 triton 3.5.0 torchaudio 2.9.0 torchvision 0.24.0 xfuser 0.4.5 xgrammar 0.1.25	Ubuntu 24.04 Python 3.12 Torch 2.9.0+cu130 CUDA 13.0.2 diffusers 0.35.2 decord 0.6.0 decord2 2.0.0 flash_attn 2.8.3 flashinfer-python 0.5.2 imageio 2.37.2 imageio-ffmpeg 0.6.0 ray 2.51.1 transformers 4.57.1 sgl-kernel 0.3.17.post1 sglang 0.5.5.post3 xgrammar 0.1.25 triton 3.5.0 torchao 0.9.0 torchaudio 2.9.0 torchvision 0.24.0 xfuser 0.4.5 ljperf 0.1.0+d0e4a408	Ubuntu 24.04 Python 3.12 Torch 2.9.0+cu130 CUDA 13.0.2 diffusers 0.35.2 decord2 2.0.0 flashinfer-python 0.5.2 imageio 2.37.2 flash_attn 2.8.3 imageio-ffmpeg 0.6.0 transformers 4.57.1 sgl-kernel 0.3.17.post1 sglang 0.5.5.post3 xgrammar 0.1.25 triton 3.5.0 torchao 0.9.0 torchaudio 2.9.0 torchvision 0.24.0 xfuser 0.4.5

Assets

Public images

CUDA 12.8 assets

egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:25.11-vllm0.11.1-pytorch2.9-cu128-20251120-serverless
egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:25.11-sglang0.5.5.post3-pytorch2.9-cu128-20251121-serverless

CUDA 13.0 assets

egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:25.11-vllm0.11.1-pytorch2.9-cu130-20251120-serverless
egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:25.11-sglang0.5.5.post3-pytorch2.9-cu130-20251121-serverless

VPC images

All existing and new ACS AI container images in the egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun repository support IN-VPC pulling.

Replace the public network registry host in your image URI with the region-specific VPC endpoint.

URI component	Public network	IN-VPC
Registry host	`egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com`	`acs-registry-vpc.{region-id}.cr.aliyuncs.com`
Repository	`egslingjun`	`egslingjun` (unchanged)
Image and tag	`{image:tag}`	`{image:tag}` (unchanged)

Replace {region-id} with the ID of the region where your ACS service runs. For example:

Region	Region ID
China (Beijing)	`cn-beijing`
China (Ulanqab)	`cn-wulanchabu`

For the full list of supported regions, see Regions.

Example {image:tag} values:

inference-nv-pytorch:25.10-vllm0.11.0-pytorch2.8-cu128-20251028-serverless
training-nv-pytorch:25.10-serverless

Note

These images are for ACS and multi-tenant Lingjun environments. They are not for single-tenant Lingjun environments.

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 an inference-nv-pytorch image using Docker and test an inference service with the Qwen2.5-7B-Instruct model.

Note

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

Run a test to verify the vLLM chat inference feature.

Start the server.

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

Send a test request from 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."}
    ]}'

For more information about vLLM, see the vLLM documentation.

Known issues

The deepgpu-comfyui plug-in, which accelerates video generation for Wanx models, supports only the GN8IS, G49E, and G59 instance types.

Container Compute Service:inference-nv-pytorch 25.11