6b7d9b76f1
### What this PR does / why we need it?
**Background:**
PR https://github.com/vllm-project/vllm-ascend/pull/6448 has introduced
a `seq_lens` CPU cache mechanism, which will considerably benefit the
performance for VL models but may lead to accuracy issues. Thus, we have
reverted it.
**Proposed Change:**
In PR https://github.com/vllm-project/vllm/pull/36605, we have supported
custom processing logic for OOT MMEncoder kernels in vLLM. Thus, we can
pre-compute `seq_lens` (rather than `cu_seqlens`) and put it on CPU
before ViT vision blocks to avoid redundant computation.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
#### ✅ Functional Test
Run Qwen2.5-VL:
```bash
vllm serve /root/.cache/modelscope/hub/models/Qwen/Qwen2.5-VL-7B-Instruct \
--max-model-len 16384 \
--max-num-batched-tokens 16384 \
--limit-mm-per-prompt '{"image": 1}'
```
Output:
```bash
"The text in the illustration is \"TONGYI Qwen.\" The word \"TONGYI\" is written in blue, and \"Qwen\" is written in gray. The font appears to be modern and clean, with \"TONGYI\" having a slightly bolder and more prominent appearance compared to \"Qwen.\" The overall design is simple and professional."
```
> [!NOTE]
> Since PR https://github.com/vllm-project/vllm/pull/36605 only modified
`Qwen3-VL` modeling files, this PR has no affect to `Qwen2.5-VL` model.
---
Run Qwen3-VL:
```bash
vllm serve /root/.cache/modelscope/hub/models/Qwen/Qwen3-VL-8B-Instruct \
--max-model-len 16384 \
--max-num-batched-tokens 16384 \
--limit-mm-per-prompt '{"image": 1}'
```
Output:
```bash
"The text in the illustration is **“TONGYI Qwen”**.\n\n### How it looks:\n- **“TONGYI”** is written in **uppercase letters** in a **bold, modern sans-serif font**, colored **blue**.\n- **“Qwen”** is written in **lowercase letters** in a **slightly thinner, elegant sans-serif font**, colored **dark gray**.\n- The two lines of text are stacked vertically, with TONG."
```
---
#### ✅ Benchmark
Launch the server:
```
vllm serve /root/.cache/modelscope/hub/models/Qwen/Qwen3-VL-8B-Instruct \
--dtype bfloat16 \
--limit-mm-per-prompt '{"image": 1}' \
--max-model-len 16384 \
--max-num-batched-tokens 16384
```
Run benchmark:
```
vllm bench serve \
--model /root/.cache/modelscope/hub/models/Qwen/Qwen3-VL-8B-Instruct \
--backend openai-chat \
--endpoint /v1/chat/completions \
--dataset-name hf \
--hf-split train \
--dataset-path lmarena-ai/vision-arena-bench-v0.1 \
--num-prompts 500 \
--request-rate 10 \
--burstiness 5 \
--no-stream
```
Before this PR:
```
============ Serving Benchmark Result ============
Successful requests: 500
Failed requests: 0
Request rate configured (RPS): 10.00
Benchmark duration (s): 78.58
Total input tokens: 33418
Total generated tokens: 61431
Request throughput (req/s): 6.36
Output token throughput (tok/s): 781.78
Peak output token throughput (tok/s): 2475.00
Peak concurrent requests: 383.00
Total token throughput (tok/s): 1207.07
---------------Time to First Token----------------
Mean TTFT (ms): 7116.24
Median TTFT (ms): 4295.84
P99 TTFT (ms): 18370.87
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 245.78
Median TPOT (ms): 264.03
P99 TPOT (ms): 334.38
---------------Inter-token Latency----------------
Mean ITL (ms): 246.99
Median ITL (ms): 117.71
P99 ITL (ms): 1327.55
==================================================
```
After this PR:
```
============ Serving Benchmark Result ============
Successful requests: 500
Failed requests: 0
Request rate configured (RPS): 10.00
Benchmark duration (s): 77.44
Total input tokens: 33418
Total generated tokens: 61522
Request throughput (req/s): 6.46
Output token throughput (tok/s): 794.40
Peak output token throughput (tok/s): 2691.00
Peak concurrent requests: 369.00
Total token throughput (tok/s): 1225.91
---------------Time to First Token----------------
Mean TTFT (ms): 6888.64
Median TTFT (ms): 4128.82
P99 TTFT (ms): 17487.94
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 240.14
Median TPOT (ms): 259.18
P99 TPOT (ms): 313.15
---------------Inter-token Latency----------------
Mean ITL (ms): 241.84
Median ITL (ms): 121.08
P99 ITL (ms): 1470.33
==================================================
```
**Performance Metrics:**
| Metric | Before this PR | After this PR | Comparison |
| :----- | :------------- | :------------ | :--------- |
| **Throughput** | | | |
| Request throughput (req/s) | 6.36 | 6.46 | +1.57% ↑ |
| Output token throughput (tok/s) | 781.78 | 794.40 | +1.61% ↑ |
| Total token throughput (tok/s) | 1,207.07 | 1,225.91 | +1.56% ↑ |
| Peak output token throughput (tok/s) | 2,475 | 2,691 | +8.73% ↑ |
| **Latency** | | | |
| Benchmark duration (s) | 78.58 | 77.44 | -1.45% ↓ |
| Mean TTFT (ms) | 7,116.24 | 6,888.64 | -3.20% ↓ |
| Median TTFT (ms) | 4,295.84 | 4,128.82 | -3.89% ↓ |
| P99 TTFT (ms) | 18,370.87 | 17,487.94 | -4.81% ↓ |
| Mean TPOT (ms) | 245.78 | 240.14 | -2.29% ↓ |
| Median TPOT (ms) | 264.03 | 259.18 | -1.84% ↓ |
| P99 TPOT (ms) | 334.38 | 313.15 | -6.35% ↓ |
| Mean ITL (ms) | 246.99 | 241.84 | -2.09% ↓ |
| Median ITL (ms) | 117.71 | 121.08 | +2.86% ↑ |
| P99 ITL (ms) | 1,327.55 | 1,470.33 | +10.76% ↑ |
**🤖 AI Summary:**
- The most notable improvement is in P99 TPOT, which dropped **-6.35%**
from 334.38ms → 313.15ms, indicating reduced tail latency for per-token
generation under heavy load.
- TTFT improved across all percentiles: mean dropped **-3.20%** (7,116ms
→ 6,889ms), median **-3.89%** (4,296ms → 4,129ms), and P99 **-4.81%**
(18,371ms → 17,488ms), reflecting faster time-to-first-token across the
board.
- TPOT also improved consistently, with mean down **-2.29%** (245.78ms →
240.14ms) and median down **-1.84%** (264.03ms → 259.18ms), showing a
modest but steady reduction in per-token generation time.
- Throughput saw a slight uplift of roughly **+1.6%** across request,
output token, and total token throughput. Peak output token throughput
jumped **+8.73%** (2,475 → 2,691 tok/s), suggesting better burst
handling capacity.
- P99 ITL increased **+10.76%** (1,328ms → 1,470ms), the largest
regression in the run. Median ITL also ticked up **+2.86%** (117.71ms →
121.08ms). These tail-latency spikes may reflect scheduling variability
under peak concurrency and could be within run-to-run noise, but are
worth monitoring.
- Overall, the PR delivers a consistent improvement in both throughput
and latency, with the caveat that P99 inter-token latency regressed —
likely a transient effect given that mean ITL still improved by
**-2.09%**.
---
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
vLLM Ascend Plugin
| About Ascend | Documentation | #SIG-Ascend | Users Forum | Weekly Meeting |
English | 中文
Latest News 🔥
- [2026/02] We released the new official version v0.13.0! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
- [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 deploying 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 the 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-Experts (MoE), 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 == 8.5.0 (Ascend HDK version refers to here)
- PyTorch == 2.9.0, torch-npu == 2.9.0
- vLLM (the same version as vllm-ascend)
Getting Started
Please use the following recommended versions to get started quickly:
| Version | Release type | Doc |
|---|---|---|
| v0.17.0rc1 | Latest release candidate | See QuickStart and Installation for more details |
| v0.13.0 | Latest stable version | See QuickStart and Installation for more details |
Contributing
See CONTRIBUTING for more details, which is a step-by-step guide to help you set up the 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 a main branch and a dev branch.
- main: main branch, corresponds to the vLLM main branch, and is continuously monitored for quality through Ascend CI.
- releases/vX.Y.Z: development branch, created alongside new releases of vLLM. For example,
releases/v0.13.0is the dev branch for vLLMv0.13.0version.
Below are the maintained branches:
| Branch | Status | Note |
|---|---|---|
| main | Maintained | CI commitment for vLLM main branch and vLLM v0.17.0 tag |
| v0.7.1-dev | Unmaintained | Only doc fixes are allowed |
| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version, only bug fixes are allowed, and no new release tags anymore. |
| 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 |
| releases/v0.13.0 | Maintained | CI commitment for vLLM 0.13.0 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.