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ais-bench

Evaluate AI model accuracy and performance on Ascend NPU with AISBench.

156|53|Updated Feb 14, 2026
Search Tags:#performance#multimodal#benchmark#vllm#ai-evaluation#ascend-npu#aisbench
One-click install
npx skills add https://github.com/ascend-ai-coding/awesome-ascend-skills --skill ais-bench
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ais-bench
Source: https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/ais-bench
Command: npx skills add https://github.com/ascend-ai-coding/awesome-ascend-skills --skill ais-bench

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

AISBench Benchmark helps teams evaluate AI models on Ascend NPU across accuracy and performance, including service/local models, latency, throughput, and real-world benchmarking scenarios.

Core Features & Use Cases

  • Supports accuracy evaluation for service and local models across text and multimodal datasets.
  • Measures performance (latency, throughput, stress testing, steady-state, real-time traffic) and provides 15+ benchmarks.
  • Provides quick-start templates and CLI to run end-to-end evaluations and generate reports.

Quick Start

Execute AISBench with your chosen models and datasets to start evaluation.

Frequently Asked Questions about ais-bench

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate AI model performance on Ascend NPU?▼

AISBench evaluates AI model accuracy and performance on Ascend NPU by measuring latency, throughput, and steady-state traffic across local and service models to generate comprehensive benchmarking reports.

Can I run multimodal benchmarks with vLLM on Ascend NPU?▼

Yes, AISBench supports multimodal benchmark evaluation alongside text datasets. It quantifies accuracy and performance for service and local models on Ascend NPU, requiring a running inference service to execute the evaluation.

What Python version is required for Ascend NPU benchmark testing?▼

Ascend NPU benchmark testing with AISBench requires a compatible Python version between 3.10 and 3.12. You must also ensure the AISBench environment is properly installed before running any evaluations.

What is the best way to measure inference latency and throughput for local models?▼

The best way to measure inference latency and throughput for local models is using AISBench's CLI and quick-start templates, which support stress testing and real-world traffic scenarios to provide over 15 distinct benchmarks.

Does AI evaluation on Ascend NPU support real-world traffic scenarios?▼

Yes, AI evaluation on Ascend NPU supports real-world traffic scenarios. AISBench measures steady-state performance and real-time traffic to quantify how models handle dynamic inference loads.

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