PD Disaggregation

PD Disaggregation#

Why and What is PD Disaggregation?#

Large Language Model (LLM) inference comprises two distinct phases: Prefill and Decode. The Prefill phase is computation-intensive, processing the entire input sequence, while the Decode phase is memory-intensive, managing the Key-Value (KV) cache for token generation. Traditionally, these phases are handled within a unified engine, where combined scheduling of prefill and decode batches introduces inefficiencies. To address these challenges, we introduce Prefill and Decoding (PD) Disaggregation in SGLang.

Issues with Unified Scheduling#

The conventional unified engine, which processes prefill and decode batches together, results in two significant problems:

  1. Prefill Interruption: Incoming prefill batches frequently interrupt ongoing decode batches, causing substantial delays in token generation.

  2. DP Attention Imbalance: In data-parallel (DP) attention, one DP worker may process a prefill batch while another handles a decode batch simultaneously, leading to increased decode latency.

PD Disaggregation resolves these by separating the two stages, enabling tailored optimizations for each.

For the design details, please refer to link.

Currently, we support Mooncake and NIXL as the transfer engine.

Mooncake#

Requirements#

uv pip install mooncake-transfer-engine

Usage#

Llama Single Node#

$ python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --disaggregation-mode prefill --disaggregation-ib-device mlx5_roce0
$ python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --disaggregation-mode decode --port 30001 --base-gpu-id 1 --disaggregation-ib-device mlx5_roce0
$ python -m sglang.srt.disaggregation.mini_lb --prefill http://127.0.0.1:30000 --decode http://127.0.0.1:30001 --host 0.0.0.0 --port 8000

DeepSeek Multi-Node#

# prefill 0
$ python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 --disaggregation-ib-device ${device_name} --disaggregation-mode prefill --host ${local_ip} --port 30000 --trust-remote-code --dist-init-addr ${prefill_master_ip}:5000 --nnodes 2 --node-rank 0 --tp-size 16 --dp-size 8 --enable-dp-attention --enable-deepep-moe --deepep-mode normal --mem-fraction-static 0.8
# prefill 1
$ python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 --disaggregation-ib-device ${device_name} --disaggregation-mode prefill --host ${local_ip} --port 30000 --trust-remote-code --dist-init-addr ${prefill_master_ip}:5000 --nnodes 2 --node-rank 1 --tp-size 16 --dp-size 8 --enable-dp-attention --enable-deepep-moe --deepep-mode normal --mem-fraction-static 0.8
# decode 0
$ python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 --disaggregation-ib-device ${device_name} --disaggregation-mode decode --host ${local_ip} --port 30001 --trust-remote-code --dist-init-addr ${decode_master_ip}:5000 --nnodes 2 --node-rank 0 --tp-size 16 --dp-size 8 --enable-dp-attention --enable-deepep-moe --deepep-mode low_latency --mem-fraction-static 0.8 --max-running-requests 128
# decode 1
$ python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 --disaggregation-ib-device ${device_name} --disaggregation-mode decode --host ${local_ip} --port 30001 --trust-remote-code --dist-init-addr ${decode_master_ip}:5000 --nnodes 2 --node-rank 1 --tp-size 16 --dp-size 8 --enable-dp-attention --enable-deepep-moe --deepep-mode low_latency --mem-fraction-static 0.8 --max-running-requests 128
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