Supported Features

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

Functional call: https://docs.vllm.ai/en/latest/features/tool_calling/

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

• 🟢 Functional: Fully operational, with ongoing optimizations.

• 🔵 Experimental: Experimental support, interfaces and functions may change.

• 🚧 WIP: Under active development, will be supported soon.

• 🟡 Planned: Scheduled for future implementation (some may have open PRs/RFCs).

• 🔴 NO plan/Deprecated: No plan or deprecated by vLLM.