kvcached virtualizes GPU memory for LLM serving

kvcached targets a real infrastructure mismatch: request-driven KV-cache demand rarely justifies reserving a large, fixed VRAM slab at startup.

• –Demand-paged physical allocation can improve GPU utilization when traffic fluctuates or multiple models share one card.
• –The project reports 2–28x lower time to first token in a three-model Llama-3.1-8B benchmark on an A100, though operators should validate those gains on their own traces. [GitHub](https://github.com/ovg-project/kvcached)
• –Its OS-style design complements, rather than replaces, engine-level techniques such as vLLM’s PagedAttention.
• –vLLM and SGLang integrations lower adoption friction, but version compatibility and production stability remain the key risks.
• –The bigger idea is GPU memory pooling: inference capacity becomes elastic infrastructure instead of a per-process reservation. [Red Hat](https://www.redhat.com/en/blog/running-llms-dynamically-production-limited-resources-hard-we-think-theres-room-another-approach)