vLLM Ascend: Run High-Throughput LLM Inference on Huawei Ascend NPUs
vLLM Ascend brings the vLLM inference engine to Huawei Ascend NPUs. Explore features, hardware support, and why Ascend is a serious GPU alternative for self-hosted LLM serving.
vLLM Ascend is the community-maintained hardware plugin that brings the industry-standard vLLM inference engine to Huawei Ascend NPUs. If you want to serve large language models at high throughput without depending on NVIDIA GPUs, this is the missing piece: it gives you the exact same PagedAttention-based serving stack — continuous batching, tensor parallelism, OpenAI-compatible API — running natively on Ascend 910B and 310P hardware.
🚀 Want to deploy vLLM Ascend yourself?
Docker configs, system requirements, and installation guides — all on one page.
View vLLM Ascend Tool Page →
Why Ascend NPUs?
Huawei Ascend NPUs (910B, 310P) are designed for AI inference with a strong performance-per-watt ratio. For self-hosters, they are a serious alternative to GPU clusters: lower acquisition cost, lower power draw, and a growing software ecosystem. The catch has always been framework support — which is exactly what vLLM Ascend solves by acting as a hardware backend inside vLLM itself.
Key Features
| Feature | What you get |
|---|---|
| Full vLLM parity | Continuous batching, PagedAttention, prefix caching |
| Hardware support | Ascend 910B and 310P, NPU memory management |
| OpenAI-compatible API | Drop-in replacement for /v1/chat/completions |
| Official Docker image | ascendai/vllm-ascend with 315K+ pulls |
Project Health
Licensed under Apache-2.0 with 2.6K+ GitHub stars, the project is actively maintained by the vLLM community and tracks upstream vLLM releases closely. Version alignment is handled by the companion vllm-ascend Python package, so upgrades stay predictable.
🚀 Ready to run LLMs on Ascend?
Get the Docker Compose config and full system requirements.
View vLLM Ascend Tool Page →