[PR #173] [MERGED] Feature: LL nvlink p2p #448

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opened 2026-10-26 10:00:51 +00:00 by bsakaguchi · 0 comments
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📋 Pull Request Information

Original PR: https://github.com/deepseek-ai/DeepEP/pull/173
Author: @cywork121
Created: 5/22/2025
Status: ✅ Merged
Merged: 5/23/2025
Merged by: @LyricZhao

Base: main ← Head: DeepEP/ll_nvlink_load_store


📝 Commits (1)

📊 Changes

2 files changed (+34 additions, -5 deletions)

View changed files

📝 csrc/kernels/internode_ll.cu (+32 -4)
📝 deep_ep/buffer.py (+2 -1)

📄 Description

This PR introduces the use of NVLink P2P copy for intra-node data transfer in low-latency scenarios. For smaller-scale decode instances requirements, we found this approach enhances the overall decoding throughput. Specifically in our inference service, the 4-node decode efficiency demonstrates a 27% improvement.

The following test data was obtained using H20:

Dispatch #EP Latency(us) bandwidth(GB/s) Combine #EP Latency(us) bandwidth(GB/s)
8 172 👉🏻 37 43.5 👉🏻 203.2 8 319 👉🏻 59 45.5 👉🏻 244.2
16 193 👉🏻 119 38.8 👉🏻 62.9 16 335 👉🏻 199 43.3 👉🏻 72.7
32 198 👉🏻 157 37.8 👉🏻 47.8 32 344 👉🏻 280 42.1 👉🏻 51.7
64 203 👉🏻 176 36.9 👉🏻 42.6 64 351 👉🏻 318 41.3 👉🏻 45.6

🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.

## 📋 Pull Request Information **Original PR:** https://github.com/deepseek-ai/DeepEP/pull/173 **Author:** [@cywork121](https://github.com/cywork121) **Created:** 5/22/2025 **Status:** ✅ Merged **Merged:** 5/23/2025 **Merged by:** [@LyricZhao](https://github.com/LyricZhao) **Base:** `main` ← **Head:** `DeepEP/ll_nvlink_load_store` --- ### 📝 Commits (1) - [`0c02143`](https://github.com/deepseek-ai/DeepEP/commit/0c02143a9d4e551b3b800a6327fe8eb3af514f7a) Feature: LL nvlink p2p ### 📊 Changes **2 files changed** (+34 additions, -5 deletions) <details> <summary>View changed files</summary> 📝 `csrc/kernels/internode_ll.cu` (+32 -4) 📝 `deep_ep/buffer.py` (+2 -1) </details> ### 📄 Description This PR introduces the use of NVLink P2P copy for intra-node data transfer in low-latency scenarios. For smaller-scale decode instances requirements, we found this approach enhances the overall decoding throughput. Specifically in our inference service, the 4-node decode efficiency demonstrates a 27% improvement. The following test data was obtained using H20: Dispatch #EP | Latency(us) | bandwidth(GB/s) | Combine #EP | Latency(us) | bandwidth(GB/s) -- | -- | -- | -- | -- | -- 8 | 172 👉🏻 37 | 43.5 👉🏻 203.2 | 8 | 319 👉🏻 59 | 45.5 👉🏻 244.2 16 | 193 👉🏻 119 | 38.8 👉🏻 62.9 | 16 | 335 👉🏻 199 | 43.3 👉🏻 72.7 32 | 198 👉🏻 157 | 37.8 👉🏻 47.8 | 32 | 344 👉🏻 280 | 42.1 👉🏻 51.7 64 | 203 👉🏻 176 | 36.9 👉🏻 42.6 | 64 | 351 👉🏻 318 | 41.3 👉🏻 45.6 --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
bsakaguchi 2026-10-26 10:00:51 +00:00
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deepseek-ai/DeepEP#448
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