inference-nv-pytorch 25.10-Container Compute Service(ACS)-阿里云帮助中心

What's new

Key features

Two images are now available for different CUDA versions:
- The CUDA 12.8 image supports only the amd64 architecture.
- The CUDA 13.0 image supports both the amd64 and aarch64 architectures.
In the CUDA 12.8 image, deepgpu-comfyui is upgraded to 1.3.0, and the deepgpu-torch optimization component is upgraded to 0.1.6+torch2.8.0cu128.
In the CUDA 13.0 image, PyTorch is upgraded to 2.9.0.
In both the CUDA 12.8 and CUDA 13.0 images, vLLM is upgraded to v0.11.0, and SGLang is upgraded to v0.5.4.

Bug fixes

No bug fixes are included in this release.

inference-nv-pytorch
Tag	25.10-vllm0.11.0-pytorch2.8-cu128-20251028-serverless	25.10-sglang0.5.4-pytorch2.8-cu128-20251027-serverless	25.10-vllm0.11.0-pytorch2.9-cu130-20251028-serverless		25.10-sglang0.5.4-pytorch2.9-cu130-20251028-serverless
Supported architectures	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 2.8.0+cu128 CUDA 12.8 diffusers 0.35.2 deepgpu-comfyui 1.3.0 deepgpu-torch 0.1.6+torch2.8.0cu128 flash_attn 2.8.3 imageio 2.37.0 imageio-ffmpeg 0.6.0 ray 2.50.1 transformers 4.57.1 triton 3.4.0 tokenizers 0.22.1 torchaudio 2.8.0+cu128 torchsde 0.2.6 torchvision 0.23.0+cu128 vllm 0.11.0 xfuser 0.4.4 xgrammar 0.1.25 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.8.0+cu128 CUDA 12.8 diffusers 0.35.2 decord 0.6.0 decord2 2.0.0 deepgpu-comfyui 1.3.0 deepgpu-torch 0.1.6+torch2.8.0cu128 flash_attn 2.8.3 flash_mla 1.0.0+1858932 flashinfer-python 0.4.1 imageio 2.37.0 imageio-ffmpeg 0.6.0 transformers 4.57.1 sgl-kernel 0.3.16.post3 sglang 0.5.4 xgrammar 0.1.25 triton 3.4.0 torchao 0.9.0 torchaudio 2.8.0+cu128 torchsde 0.2.6 torchvision 0.23.0+cu128 xfuser 0.4.4 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.9.0+cu130 CUDA 13.0.1 diffusers 0.35.2 flash_attn 2.8.3 imageio 2.37.0 imageio-ffmpeg 0.6.0 ray 2.50.1 transformers 4.57.1 triton 3.5.0 tokenizers 0.22.1 torchvision 0.24.0+cu130 vllm 0.11.0 xfuser 0.4.4 xgrammar 0.1.25 ljperf 0.1.0+d0e4a408	Ubuntu 24.04 Python 3.12 Torch 2.9.0+cu130 CUDA 13.0.1 diffusers 0.35.2 flash_attn 2.8.3 transformers 4.57.1 ray 2.50.1 vllm 0.11.0 Triton 3.5.0 tokenizers 0.22.1 torchaudio 2.9.0 torchvision 0.24.0 xfuser 0.3 xgrammar 0.1.25 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.9.0+cu130 CUDA 13.0.1 diffusers 0.35.2 decord 0.6.0 decord2 2.0.0 flash_attn 2.8.3 flash_mla 1.0.0+1858932 flashinfer-python 0.4.1 imageio 2.37.0 imageio-ffmpeg 0.6.0 transformers 4.57.1 sgl-kernel 0.3.16.post3 sglang 0.5.4 xgrammar 0.1.25 triton 3.5.0 torchao 0.9.0 torchaudio 2.9.0+cu130 torchvision 0.24.0+cu130 xfuser 0.4.4 ljperf 0.1.0+477686c5	Ubuntu 24.04 Python 3.12 Torch 2.9.0+cu130 CUDA 13.0.1 diffusers 0.35.2 decord2 2.0.0 flashinfer-python 0.4.1 imageio 2.37.0 imageio-ffmpeg 0.6.0 transformers 4.57.1 sgl-kernel 0.3.16.post3 sglang 0.5.4 xgrammar 0.1.25 triton 3.5.0 torchao 0.9.0 torchaudio 2.9.0 torchvision 0.24.0 xfuser 0.4.4

Assets

Public images

CUDA 12.8

egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:25.10-vllm0.11.0-pytorch2.8-cu128-20251028-serverless
egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:25.10-sglang0.5.4-pytorch2.8-cu128-20251027-serverless

CUDA 13.0

egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:25.10-vllm0.11.0-pytorch2.9-cu130-20251028-serverless
egslingjun-registry.cn-wulanchabu.cr.aliyuncs.com/egslingjun/inference-nv-pytorch:25.10-sglang0.5.4-pytorch2.9-cu130-20251028-serverless

VPC image

acs-registry-vpc.{region-id}.cr.aliyuncs.com/egslingjun/{image:tag}

Replace {region-id} with the region where your Alibaba Cloud Container Compute Service (ACS) is activated (for example, cn-beijing or cn-wulanchabu).

Replace {image:tag} with the name and tag of the image.

Note

The inference-nv-pytorch:25.03-vllm0.8.2-pytorch2.6-cu124-20250328-serverless and inference-nv-pytorch:25.03-sglang0.4.4.post1-pytorch2.5-cu124-20250327-serverless images are compatible with ACS and Lingjun multi-tenant deployments only. It is not compatible with Lingjun single-tenant deployments.

Driver requirements

CUDA 12.8: Requires NVIDIA Driver release 570 or later.
CUDA 13.0: Requires NVIDIA Driver release 580 or later.

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

For more information about building model inference services with Alibaba Cloud Container Service GPU compute power, see the following topics:

Pull the inference container image.

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

Download the open-source model in ModelScope format.

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 an inference test to validate the vLLM chat capability.

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": "Tell me about 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 neural systems, especially how neurons interact in the brain. Deep learning uses deep neural networks to process and analyze large amounts of data to learn effective predictive models. This technology has achieved strong results in image recognition, speech recognition, natural language processing, and more.\n\nIn deep learning, a neural network has multiple layers, including an input layer, one or more hidden layers, and an output layer. Each layer contains many nodes (neurons). Nodes connect to nodes in other layers through weighted links. During training, the network adjusts these weights to minimize the error between predicted outputs and actual outputs. This process typically uses optimization methods such as gradient descent.\n\nTraining deep learning models requires large datasets and significant compute resources. In recent years, advances in hardware (such as GPUs and TPUs) and rapid growth in datasets have helped deep learning spread quickly. In addition to the applications above, deep learning is widely used in medical diagnosis, autonomous driving, gaming, finance, and many other fields."},"tool_calls":[],"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":237,"completion_tokens":213}}

For more information about vLLM, see vLLM.

Known issues

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