- Ascend 910, Ascend 910 Pro B (Ascend-cann-kernels-910) unplanned yet
From a technical view, vllm-ascend support would be possible if the torch-npu is supported. Otherwise, we have to implement it by using custom ops. We are also welcome to join us to improve together.
You can get our containers at `Quay.io`, e.g., [<u>vllm-ascend</u>](https://quay.io/repository/ascend/vllm-ascend?tab=tags) and [<u>cann</u>](https://quay.io/repository/ascend/cann?tab=tags).
If you are in China, you can use `daocloud` to accelerate your downloading:
There are many channels that you can communicate with our community developers / users:
- Submit a GitHub [<u>issue</u>](https://github.com/vllm-project/vllm-ascend/issues?page=1).
- Join our [<u>weekly meeting</u>](https://docs.google.com/document/d/1hCSzRTMZhIB8vRq1_qOOjx4c9uYUxvdQvDsMV2JcSrw/edit?tab=t.0#heading=h.911qu8j8h35z) and share your ideas.
- Join our [<u>WeChat</u>](https://github.com/vllm-project/vllm-ascend/issues/227) group and ask your quenstions.
- Join our ascend channel in [<u>vLLM forums</u>](https://discuss.vllm.ai/c/hardware-support/vllm-ascend-support/6) and publish your topics.
### 5. What features does vllm-ascend V1 supports?
Find more details [<u>here</u>](https://github.com/vllm-project/vllm-ascend/issues/414).
Currently, only some models are improved. Such as `Qwen2 VL`, `Deepseek V3`. Others are not good enough. From 0.9.0rc2, Qwen and Deepseek works with graph mode to play a good performance. What's more, you can install `mindie-turbo` with `vllm-ascend v0.7.3` to speed up the inference as well.
vllm-ascend is a plugin for vllm. Basically, the version of vllm-ascend is the same as the version of vllm. For example, if you use vllm 0.7.3, you should use vllm-ascend 0.7.3 as well. For main branch, we will make sure `vllm-ascend` and `vllm` are compatible by each commit.
Currently, w8a8 quantization is already supported by vllm-ascend originally on v0.8.4rc2 or higher, If you're using vllm 0.7.3 version, w8a8 quantization is supporeted with the integration of vllm-ascend and mindie-turbo, please use `pip install vllm-ascend[mindie-turbo]`.
Please following the [quantization inferencing tutorail](https://vllm-ascend.readthedocs.io/en/main/tutorials/multi_npu_quantization.html) and replace model to DeepSeek.
If you're using vllm 0.7.3 version, this is a known progress bar display issue in VLLM, which has been resolved in [this PR](https://github.com/vllm-project/vllm/pull/12428), please cherry-pick it locally by yourself. Otherwise, please fill up an issue.
- **Functional test**: we added CI, includes portion of vllm's native unit tests and vllm-ascend's own unit tests，on vllm-ascend's test, we test basic functionality、popular models availability and [supported features](https://vllm-ascend.readthedocs.io/en/latest/user_guide/suppoted_features.html) via e2e test
- **Performance test**: we provide [benchmark](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks) tools for end-to-end performance benchmark which can easily to re-route locally, we'll publish a perf website like [vllm](https://simon-mo-workspace.observablehq.cloud/vllm-dashboard-v0/perf) does to show the performance test results for each pull request
- **Accuracy test**: we're working on adding accuracy test to CI as well.
Finnall, for each release, we'll publish the performance test and accuracy test report in the future.
### 14. How to fix the error "InvalidVersion" when using vllm-ascend?
It's usually because you have installed an dev/editable version of vLLM package. In this case, we provide the env variable `VLLM_VERSION` to let users specify the version of vLLM package to use. Please set the env variable `VLLM_VERSION` to the version of vLLM package you have installed. The format of `VLLM_VERSION` should be `X.Y.Z`.
OOM errors typically occur when the model exceeds the memory capacity of a single NPU. For general guidance, you can refer to [vLLM's OOM troubleshooting documentation](https://docs.vllm.ai/en/latest/getting_started/troubleshooting.html#out-of-memory).
In scenarios where NPUs have limited HBM (High Bandwidth Memory) capacity, dynamic memory allocation/deallocation during inference can exacerbate memory fragmentation, leading to OOM. To address this:
- **Adjust `--gpu-memory-utilization`**: If unspecified, will use the default value of `0.9`. You can decrease this param to reserve more memory to reduce fragmentation risks. See more note in: [vLLM - Inference and Serving - Engine Arguments](https://docs.vllm.ai/en/latest/serving/engine_args.html#vllm.engine.arg_utils-_engine_args_parser-cacheconfig).
- **Configure `PYTORCH_NPU_ALLOC_CONF`**: Set this environment variable to optimize NPU memory management. For example, you can `export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True` to enable virtual memory feature to mitigate memory fragmentation caused by frequent dynamic memory size adjustments during runtime, see more note in: [PYTORCH_NPU_ALLOC_CONF](https://www.hiascend.com/document/detail/zh/Pytorch/700/comref/Envvariables/Envir_012.html).
### 15. Failed to enable NPU graph mode when running DeepSeek?
You may encounter the following error if running DeepSeek with NPU graph mode enabled. The allowed number of queries per kv when enabling both MLA and Graph mode only support {32, 64, 128}, **Thus this is not supported for DeepSeek-V2-Lite**, as it only has 16 attention heads. The NPU graph mode support on DeepSeek-V2-Lite will be done in the future.
And if you're using DeepSeek-V3 or DeepSeek-R1, please make sure after the tensor parallel split, num_heads / num_kv_heads in {32, 64, 128}.
```bash
[rank0]: RuntimeError: EZ9999: Inner Error!
[rank0]: EZ9999: [PID: 62938] 2025-05-27-06:52:12.455.807 numHeads / numKvHeads = 8, MLA only support {32, 64, 128}.[FUNC:CheckMlaAttrs][FILE:incre_flash_attention_tiling_check.cc][LINE:1218]
