3d04ae8e7d
### Motivation. **Limitations of the current vLLM v1 scheduling strategy** vLLM v1 scheduling currently enables chunkedprefill by default, which processes prefill and decode requests simultaneously in a single scheduling session. This can impact the overall system throughput and performance in some scenarios. Balance scheduling addresses this issue by synchronizing the number of running queues across all schedulers to delay the scheduling of new requests, thereby improving the overall system's steady-state decoding time. This achieves: ✅Adding `balance_gather` to the scheduler synchronizes the number of requests in the running queues between DPs. ✅Balance scheduling improves the decode steady-state time, thereby increasing the overall output throughput of the inference system. ### Proposed Change. **1.Feature Overview** In the vLLM scheduler, running requests (i.e., requests that are already undergoing pre-filled computation) have the highest priority, followed by waiting requests (i.e., requests that have not yet been computed). As shown in the diagram above, when the entire inference system exits from a steady state, the scheduler will schedule a batch of new requests for prefill operations and then synchronize them among the dynamic programming (DP) models. This can cause some DP models that are entirely decoded to synchronize with the number of prefilled tokens. Frequent prefill scheduling by certain DP models can lead to a deterioration in the overall system output throughput. Balance scheduling synchronizes the number of running queue requests across different DPs, and only schedules new requests for prefilling when at least every scheduler has fewer than max_nun_requst. **2.Implementation Design** **3.Experiment Results** - Fixed-length input scenario: In the performance test scenario with 3.5K fixed-length input and 1.5K fixed-length output, the throughput performance was improved by approximately **18%** after adding balance scheduling. | Method | Model | Input Len | Request Count | Output Len | BatchSize | Average TTFT | Average TPOT | e2e duration | Input Token Throughput | Output Token Throughput | Request Throughput | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | | Baseline | DeepSeekV3.1 | 3500 | 512 | 1500 | 128 | 6600 | 86.85 | 591.9s | 3030.5 | 1297.3 | 0.86 | | Balance scheduling | DeepSeekV3.1 | 3500 | 512 | 1500 | 128 | 7012 | 70.63 | 501.7s | 3575.7 | 1530.7 | 1.02 | **4.Demo PR** [#29721 ](https://github.com/vllm-project/vllm/pull/29721) --------- Signed-off-by: GDzhu01 <809721801@qq.com>
vLLM Ascend Plugin
| About Ascend | Documentation | #sig-ascend | Users Forum | Weekly Meeting |
English | 中文
Latest News 🔥
- [2025/12] We released the new official version v0.11.0! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
- [2025/09] We released the new official version v0.9.1! Please follow the official guide to start deploy large scale Expert Parallelism (EP) on Ascend.
- [2025/08] We hosted the vLLM Beijing Meetup with vLLM and Tencent! Please find the meetup slides here.
- [2025/06] User stories page is now live! It kicks off with LLaMA-Factory/verl//TRL/GPUStack to demonstrate how vLLM Ascend assists Ascend users in enhancing their experience across fine-tuning, evaluation, reinforcement learning (RL), and deployment scenarios.
- [2025/06] Contributors page is now live! All contributions deserve to be recorded, thanks for all contributors.
- [2025/05] We've released first official version v0.7.3! We collaborated with the vLLM community to publish a blog post sharing our practice: Introducing vLLM Hardware Plugin, Best Practice from Ascend NPU.
- [2025/03] We hosted the vLLM Beijing Meetup with vLLM team! Please find the meetup slides here.
- [2025/02] vLLM community officially created vllm-project/vllm-ascend repo for running vLLM seamlessly on the Ascend NPU.
- [2024/12] We are working with the vLLM community to support [RFC]: Hardware pluggable.
Overview
vLLM Ascend (vllm-ascend) is a community maintained hardware plugin for running vLLM seamlessly on the Ascend NPU.
It is the recommended approach for supporting the Ascend backend within the vLLM community. It adheres to the principles outlined in the [RFC]: Hardware pluggable, providing a hardware-pluggable interface that decouples the integration of the Ascend NPU with vLLM.
By using vLLM Ascend plugin, popular open-source models, including Transformer-like, Mixture-of-Expert, Embedding, Multi-modal LLMs can run seamlessly on the Ascend NPU.
Prerequisites
- Hardware: Atlas 800I A2 Inference series, Atlas A2 Training series, Atlas 800I A3 Inference series, Atlas A3 Training series, Atlas 300I Duo (Experimental)
- OS: Linux
- Software:
- Python >= 3.10, < 3.12
- CANN == 8.3.rc2 (Ascend HDK version refers to here)
- PyTorch == 2.8.0, torch-npu == 2.8.0
- vLLM (the same version as vllm-ascend)
Getting Started
Please use the following recommended versions to get started quickly:
| Version | Release type | Doc |
|---|---|---|
| v0.12.0rc1 | Latest release candidate | QuickStart and Installation for more details |
| v0.11.0 | Latest stable version | QuickStart and Installation for more details |
Contributing
See CONTRIBUTING for more details, which is a step-by-step guide to help you set up development environment, build and test.
We welcome and value any contributions and collaborations:
- Please let us know if you encounter a bug by filing an issue
- Please use User forum for usage questions and help.
Branch
vllm-ascend has main branch and dev branch.
- main: main branch，corresponds to the vLLM main branch, and is continuously monitored for quality through Ascend CI.
- vX.Y.Z-dev: development branch, created with part of new releases of vLLM. For example,
v0.7.3-devis the dev branch for vLLMv0.7.3version.
Below is maintained branches:
| Branch | Status | Note |
|---|---|---|
| main | Maintained | CI commitment for vLLM main branch and vLLM v0.13.0 tag |
| v0.7.1-dev | Unmaintained | Only doc fixed is allowed |
| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version, only bug fix is allowed and no new release tag any more. |
| v0.9.1-dev | Maintained | CI commitment for vLLM 0.9.1 version |
| v0.11.0-dev | Maintained | CI commitment for vLLM 0.11.0 version |
| rfc/feature-name | Maintained | Feature branches for collaboration |
Please refer to Versioning policy for more details.
Weekly Meeting
- vLLM Ascend Weekly Meeting: https://tinyurl.com/vllm-ascend-meeting
- Wednesday, 15:00 - 16:00 (UTC+8, Convert to your timezone)
License
Apache License 2.0, as found in the LICENSE file.