Llama.cpp Adds LRU Scheduler for Multi-Model Serving

The new LRU scheduler in llama.cpp's router represents a significant improvement for multi-model serving scenarios common in local deployment. The scheduler intelligently handles request coalescing and prevents unnecessary model evictions, ensuring smoother operation when multiple models compete for limited local resources.

For practitioners running inference servers on consumer hardware with limited VRAM, this scheduler optimization means better throughput and lower latency when switching between different models. Proper request queuing and model lifecycle management are essential for production-grade local LLM services.

Read the full article on llama.cpp release.

Source: llama.cpp release · Relevance: 9/10