Why Local LLMs Suddenly Slow Down at Long Context
📰 Hackernoon
Discover why local LLMs slow down at long context and how to address the issue
Action Steps
- Identify the point at which your local LLM's context fills up and generation speed drops
- Check if the KV cache is spilling out of VRAM into shared memory
- Monitor VRAM usage to anticipate and prevent slowdowns
- Consider increasing VRAM or optimizing model architecture to reduce memory usage
- Test and compare generation speeds at different context lengths to measure improvements
Who Needs to Know This
Machine learning engineers and developers working with local LLMs can benefit from understanding this issue to optimize their model's performance
Key Insight
💡 KV cache spilling out of VRAM into shared memory can cause significant slowdowns in local LLMs
Share This
🚨 Local LLMs slowing down at long context? 🤔 It might be the KV cache spilling out of VRAM! 💡
Key Takeaways
Discover why local LLMs slow down at long context and how to address the issue
Full Article
Your local LLM runs fine until the context fills up past a certain point - then generation speed can drop by ~50%. The cause is the KV cache spilling out of VRAM into slower shared memory. On Windows it happens silently, with no out-of-memory error to warn you.
DeepCamp AI