inference-nv-pytorch 26.02-Container Compute Service(ACS)-阿里云帮助中心

Main features and bug fixes

Main features

This release provides images for CUDA 12.8 and CUDA 13.0:
- The CUDA 12.8 images support only the amd64 architecture.
- The CUDA 13.0 images support both the amd64 and aarch64 architectures.
In the vLLM images, Torch is upgraded to 2.10.0 and vLLM is upgraded to v0.15.1.
In the SGLang images, Torch is upgraded to 2.10.0 and SGLang is upgraded to v0.5.9.

Bug fixes

None.

Image	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
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 2.10.0+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 images

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 images

To quickly pull ACS AI container images from within a VPC, replace the public 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 ID of a supported 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

These images are for ACS and the Lingjun multi-tenant offering. Do not use them 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 shows how to pull the inference-nv-pytorch image by using 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 Artifacts Center on the Create Workload page in the console, or specify the image reference in a YAML file. For more information about building a model inference service by using ACS GPU compute, see the following topics:

Pull the inference container image.

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

Use ModelScope to download the open-source model.

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]

Test the vLLM chat 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

Run a client-side test.

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:

For more information, see the vLLM documentation.

Known issues

In this release, these images do not support the deepgpu-comfyui plugin.