KV-RM: Regularizing KV-Cache Movement for Static-Graph LLM Serving
📰 ArXiv cs.AI
Learn to optimize LLM serving with KV-RM, a technique to regularize KV-cache movement for static-graph decoders, improving performance and reducing latency
Action Steps
- Implement KV-RM to regularize KV-cache movement in static-graph LLM decoders
- Use paged KV management to recover flexibility in dynamic runtimes
- Apply step-level scheduling to optimize decoding performance
- Configure static-graph executors to minimize memory over-reservation and burst-time latency
- Test and evaluate the impact of KV-RM on LLM serving performance
Who Needs to Know This
Machine learning engineers and researchers working on LLM serving and static-graph decoders can benefit from this technique to improve the efficiency and scalability of their models
Key Insight
💡 Regularizing KV-cache movement can significantly improve the performance and scalability of static-graph LLM decoders
Share This
🚀 Improve LLM serving with KV-RM! Regularize KV-cache movement for static-graph decoders and boost performance 🚀
Key Takeaways
Learn to optimize LLM serving with KV-RM, a technique to regularize KV-cache movement for static-graph decoders, improving performance and reducing latency
Full Article
Title: KV-RM: Regularizing KV-Cache Movement for Static-Graph LLM Serving
Abstract:
arXiv:2605.09735v1 Announce Type: cross Abstract: Static-graph LLM decoders provide predictable launches, fixed tensor shapes, and low submission overhead, but online decoding exposes highly irregular KV-cache behavior: request lengths differ, EOS events arrive asynchronously, and logical histories fragment over time. Dynamic runtimes recover flexibility through paged KV management and step-level scheduling, while static-graph executors often over-reserve memory and suffer burst-time latency out
Abstract:
arXiv:2605.09735v1 Announce Type: cross Abstract: Static-graph LLM decoders provide predictable launches, fixed tensor shapes, and low submission overhead, but online decoding exposes highly irregular KV-cache behavior: request lengths differ, EOS events arrive asynchronously, and logical histories fragment over time. Dynamic runtimes recover flexibility through paged KV management and step-level scheduling, while static-graph executors often over-reserve memory and suffer burst-time latency out
DeepCamp AI