MoE optimizations
Open
Nobody has claimed this yet.
Investigating
Performance
roadmap
triaged
- Dominant language
- Python
- Stars
- 14.7k
- Forks
- 2.8k
- Avg merge
- 2d 23h
- Merged PRs (30d)
- 489
Description
- General optimization
- [Ongoing] SmartRouter(targeting min-latency)
- [Ongoing] Large-scale EP (custom large-scale A2A + EP workload balancer)
- First targeting GB200 NVL72
- Extending support for EP across nodes
- [Ongoing] Multi-shot Allreduce optimization on GB200
- Optimizations for DeepSeek R1
- Per-Tensor FP8 KV Cache
- [Done] Hopper
- [Ongoing] Blackwell
- [Ongoing] KV Cache reuse
- [Ongoing] Chunked context
- [Ongoing] INT4 AWQ
- Per-Tensor FP8 KV Cache
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files, tests, or entry points are named. Begin by locating the MoE, DeepSeek R1, SmartRouter, expert-parallelism, and KV-cache implementations in the repository, then check which listed optimization still has maintainer direction. Done requires a defined target, implementation scope, and validation criteria for one optimization rather than the full roadmap.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- distributed-systems, machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100