backend.ai

Learn how KV cache offloading works in LLM serving for Agentic AI—covering architecture, data movement paths, and when offloading helps or hurts inference performance.

Over the past year, we have built two production RAG systems addressing completely different tasks. One is HSense, a multi-agent system for Korean customs item classification, and the other is the Backend.AI RAG Assistant, which processes customer support queries based on seven document projects.

DGX Spark is a desktop AI supercomputer that packs 128GB of unified memory and 1 PFLOP-class Grace Blackwell (GB10) performance into a palm-sized box. However, its internal GPU belongs to the SM12x series, distinct from the data center-grade Blackwell (SM100). This creates a subtle architectural gap: the latest LLM stacks, heavily reliant on MLA·DSA-specific kernels like GLM-5, "Blackwell support" alone doesn't guarantee immediate compatibility. This creates a subtle architectural gap requiring separate code management for Hopper, data center Blackwell, and consumer Blackwell. The engineering team examines Spark, which is based on Blackwell but features a slightly different architecture.