Deterministic KV Cache Compression for Large-Scale Agentic Workloads

How non-blocking memory quantization reduces LLM context footprint by 4.2x without precision degradation in financial reasoning tasks.

System Architecture Overview

High-concurrency LLM inference for multi-step autonomous agents is fundamentally constrained by GPU Key-Value (KV) cache memory footprints. In long-horizon context windows (up to 128k tokens), memory capacity rather than raw compute throughput becomes the primary bottleneck.