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[Doc] fix the nit in docs (#6826)

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Refresh the doc, fix the nit in the docs

- vLLM version: v0.15.0
- vLLM main:
83b47f67b1

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
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wangxiyuan
2026-02-27 11:50:27 +08:00
committed by GitHub
parent 981d803cb7
commit a95c0b8b82
30 changed files with 145 additions and 118 deletions
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4
docs/source/developer_guide/feature_guide/disaggregated_prefill.md
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@@ -7,8 +7,8 @@ This feature addresses the need to optimize the **Time Per Output Token (TPOT)**
1. **Adjusting Parallel Strategy and Instance Count for P and D Nodes**
Using the disaggregated-prefill strategy, this feature allows the system to flexibly adjust the parallelization strategy (e.g., data parallelism (dp), tensor parallelism (tp), and expert parallelism (ep)) and the instance count for both P (Prefiller) and D (Decoder) nodes. This leads to better system performance tuning, particularly for **TTFT** and **TPOT**.
2. **Optimizing TPOT**
Without disaggregated-prefill strategy, prefill tasks are inserted during decoding, which results in inefficiencies and delays. disaggregated-prefill solves this by allowing for better control over the system’s **TPOT**. By managing chunked prefill tasks effectively, the system avoids the challenge of determining the optimal chunk size and provides more reliable control over the time taken for generating output tokens.
2. **Optimizing TPOT**
Without the disaggregated-prefill strategy, prefill tasks are inserted during decoding, which results in inefficiencies and delays. Disaggregated-prefill solves this by allowing for better control over the system’s **TPOT**. By managing chunked prefill tasks effectively, the system avoids the challenge of determining the optimal chunk size and provides more reliable control over the time taken for generating output tokens.
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