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[Doc][Misc] Correcting the document and uploading the model deployment template (#8287)

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### What this PR does / why we need it?
Correcting the document and uploading the model deployment template

### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?

---------

Signed-off-by: herizhen <1270637059@qq.com>
Signed-off-by: herizhen <59841270+herizhen@users.noreply.github.com>
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herizhen
2026-04-15 16:03:11 +08:00
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parent 147b589f62
commit 95726d20eb
31 changed files with 536 additions and 308 deletions
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docs/source/user_guide/feature_guide/epd_disaggregation.md
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@@ -56,12 +56,12 @@ All related code is under `vllm/distributed/ec_transfer`.
* *Scheduler role* – checks cache existence and schedules loads.
* *Worker role* – loads the embeddings into memory.
* **EPD Load Balance Proxy** -
* **EPD Load Balancing Proxy** -
* *Multi-Path Scheduling Strategy* - dynamically diverts the multimodal request or text requests to the corresponding inference path
* *Instance-Level Dynamic Load Balancing* - dispatches multimodal requests based on a least-loaded strategy, using a priority queue to balance the active token workload across instances.
We create the example setup with the **MooncakeLayerwiseConnector** from `vllm_ascend/distributed/kv_transfer/kv_p2p/mooncake_layerwise_connector.py` and refer to the `examples/disaggregated_prefill_v1/load_balance_proxy_layerwise_server_example.py` to facilitate the kv transfer between P and D. For step-by-step deployment and configuration of Mooncake, refer to the following guide:
[https://docs.vllm.ai/projects/ascend/en/latest/tutorials/pd_disaggregation_mooncake_multi_node.html](https://docs.vllm.ai/projects/ascend/en/latest/tutorials/features/pd_disaggregation_mooncake_multi_node.html)
[https://docs.vllm.ai/projects/ascend/en/latest/tutorials/features/pd_disaggregation_mooncake_multi_node.html](https://docs.vllm.ai/projects/ascend/en/latest/tutorials/features/pd_disaggregation_mooncake_multi_node.html)
For the PD disaggregation part, when using MooncakeLayerwiseConnector: The request first enters the Decoder instance,the Decoder triggers a remote prefill task in reverse via the Metaserver. The Prefill node then executes inference and pushes KV Cache layer-wise to the Decoder, overlapping computation with transmission. Once the transfer is complete, the Decoder seamlessly continues with the subsequent token generation.
`docs/source/developer_guide/Design_Documents/disaggregated_prefill.md` shows the brief idea about the disaggregated prefill.