NVIDIA / NVIDIA/TensorRT-LLM

Disaggregated Prefill & Decode serving optimizations

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Investigating Performance roadmap triaged
Dominant language
Python
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Avg merge
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Merged PRs (30d)
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Description

Disaggregated Prefill & Decode serving

  • [Done] MPI/UCX backend integration
  • [Ongoing] NIXL Integration
  • Performance tuning
  • Best practice guide

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

No files, tests, or entry points are named. Review the NIXL integration and performance-tuning work described in the issue, then determine the serving benchmarks and documentation needed; done would include completing the remaining integration, tuning the disaggregated prefill/decode path, and publishing the best-practice guide.

Written by the indexing model from the issue text.

Assessment

Domain
backend, distributed-systems, performance
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

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