Supported Features¶

The feature support principle of vLLM Ascend is: aligned with vLLM. We are also actively collaborating with the community to accelerate support.

You can check the support status of vLLM V1 Engine. Below is the feature support status of vLLM Ascend:

Feature	Status	Next Step
Chunked Prefill	🟢 Functional	Functional, see detailed note: Chunked Prefill
Automatic Prefix Caching	🟢 Functional	Functional, see detailed note: vllm-ascend#732
LoRA	🔵 Experimental	Functional, see detailed note: LoRA
Speculative decoding	🟢 Functional	Basic support
Pooling	🔵 Experimental	CI needed to adapt to more models; V1 support relies on vLLM support.
Enc-dec	🟡 Planned	vLLM should support this feature first.
Multi Modality	🟢 Functional	Multi Modality, optimizing and adapting more models
LogProbs	🟢 Functional	CI needed
Prompt logProbs	🟢 Functional	CI needed
Async output	🟢 Functional	CI needed
Beam search	🔵 Experimental	CI needed
Guided Decoding	🟢 Functional	vllm-ascend#177
Tensor Parallel	🟢 Functional	Make TP >4 work with graph mode.
Pipeline Parallel	🟢 Functional	Write official guide and tutorial.
Expert Parallel	🟢 Functional	Support dynamic EPLB.
Data Parallel	🟢 Functional	Data Parallel support for Qwen3 MoE.
Prefill Decode Disaggregation	🟢 Functional	Functional, xPyD is supported.
Quantization	🟢 Functional	W8A8 available; working on more quantization method support (W4A8, etc)
Graph Mode	🟢 Functional	Functional, see detailed note: Graph Mode
Sleep Mode	🟢 Functional	Functional, see detailed note: Sleep Mode
Context Parallel	🟢 Functional	Functional, see detailed note: Context Parallel