HUAWEI CLOUD Ascension AI Computing Power Application Process: How Can I Quickly Deploy Large Model Inference Service in 2026?
In 2026, the demand for computing power for large models (LLM) has shifted from "laboratory alchemy" to "large-scale business reasoning". In the face of high demand for computing power, Huawei Cloud Ascension (Ascend) relies on
Rising 910 Series
(training) and
Rising 310/710 Series
The ecological maturity of (reasoning) has become the preferred base for domestic enterprises and developers to deploy large models.
If you are holding a large model mirror image, but are spinning around the tedious resource application and environment configuration, this actual combat tutorial will take you to avoid all pits and complete the deployment of reasoning services as quickly as possible.
The first stage: precise selection-apply for "calculation package" on demand"
The classification of Huawei's cloud computing market in 2026 is very detailed. Before applying, you must know what kind of architecture you need:
Acentic Cloud Server (AI Server): Suitable for projects that require a deeply customized environment (such as installing specific drivers and development frameworks).
ModelArts Studio (Big Model as a Service): Recommended first. This is the current mainstream of the industry, it directly integrates the power of ascension and Huawei's self-developed CANN (Compute Architecture for Neural Networks) software stack, out of the box.
Application process fast forward:
Registration and real name: Log on to the HUAWEI CLOUD official website to ensure that the enterprise real name authentication is completed (the amount of high-level computing power applied for enterprise accounts is higher and the approval is faster).
Go to the ModelArts management console: search for "ascension cloud computing power" and select "inference dedicated resource pool".
Specification selection: for 7B/13B model, it is recommended to select Ascend 310P/910B video memory specification (e.g. single card 32GB or 64GB); If it is a model above 10 billion level, be sure to check multi-machine multi-card distributed reasoning.
Phase 2: Environment Preparation-Configuration of CANN Software Stack
The core of rising computing power lies in
CANN
. The CANN 8.x version of 2026 is already perfectly compatible with mainstream arithmetic libraries, but in order to maximize performance, it is recommended to follow the following criteria:
1. Mirror selection
Don't install the drive from scratch! Search in ModelArts Mirror Center
"Ascend-PyTorch-Llama"
and other preset mirrors. These images are already pre-installed:
Firmware/Driver: Rising bottom drive.
MindSpore/PyTorch (Arise plugin version): Make sure the code runs on the Arise NPU instead of the CPU.
2. Model transformation (MindIE)
The secret of big model inference speed is
MindIE (Mind Inference Engine)
.
Use the atc command to convert commonly used HuggingFace formats (such as. safetensors or. bin)
Convert to an off-line model format optimized for ascension.
Tip: In 2026, HUAWEI CLOUD already supports "dynamic operators". Most mainstream models can skip the cumbersome static conversion and load directly through the vLLM-Ascend framework.
The third stage: rapid deployment reasoning service (actual step)
Assuming you have applied for computing resources, here are the "three steps" to deploying the mainstream model in 2026 ":
Step 1: Mount a Parallel File System (SFS Turbo)
Large model weights are often tens of GB, and ordinary cloud hard disks are too slow to read and write. Proposed application
SFS Turbo Cache Acceleration
mount it to the inference container's
/data
Table of contents.
Step 2: Launch the Reasoning Framework (vLLM-Ascend)
At present, the most popular reasoning engine is vLLM adapted to the rising. Execute at the container terminal:
Bash
python -m vllm.entrypoints.openai.api_server \
--model /data/your-model-path \
--device npu \
--tensor-parallel-size 2 \
--trust-remote-code
Note: -- device npu is the key, which tells the framework to abandon the graphics card and call the Ascend AI core.
Step 3: Configure Auto Scaling and API Gateway
To cope with unexpected traffic, turn on the HUAWEI CLOUD console
"Auto Scaling"
. When the NPU utilization rate exceeds 80%, the system will automatically pull up a new computing node. Finally, through
API Gateway
Map out the HTTPS port and your big model inference service is online.
Avoidance Guide to 2026: 3 Suggestions for Developers
Pay attention to PagedAttention compatibility: the driver of ascension 2026 has fully optimized long text processing. it is necessary to upgrade to the latest CANN version to open PagedAttention, otherwise long dialogue reasoning will be very stuck.
Use the "prepaid + on-demand" combination model: reasoning services are long-term, and full pay-as-you-go will make finance cry. It is recommended to purchase a "calculation package" base, combined with on-demand expansion, the cost can be reduced by about 40%.
Make good use of the ModelZoo of the ascension community: Huawei has open-sourced the optimal configuration parameters of mainstream models (DeepSeek, Llama 3, Qwen, etc.) on ascension. Don't grope yourself, go directly to the official website to copy the corresponding config.
Summary
In 2026, apply for and deploy a large model on HUAWEI CLOUD. The core logic has changed from "parameter adjustment" to "image selection + engine configuration". Just choose right
Ascend
910/310 Specifications
, use well
MindIE or vLLM Adapted Edition
, you can complete the whole process from computing power application to API call in 30 minutes.
Calculation force is not the threshold, how to use calculation force efficiently is.
Now go backstage and apply for your first rising NPU!
