The VRAM-Intelligence Tradeoff: Efficiency Limits of Working Memory in Autonomous Agents

Prasanth

Keywords: KV-cache compression, Heavy-Hitter Oracle, autonomous agents, memory efficiency, transformer optimization, VRAM management, sparse attention, foundation models

TL;DR: 87.5% memory reduction in LLM agents maintains 87% reasoning accuracy via Heavy-Hitter eviction—enabling 8× more concurrent agents on shared infrastructure.

Abstract: Large language models (LLMs) deployed as autonomous agents face a fundamental constraint: the quadratic scaling of key-value (KV) cache memory with sequence length. We investigate the practical limits of KV-cache compression on agent reasoning quality using a Heavy-Hitter Oracle (H2O) eviction policy. Profiling on NVIDIA DGX infrastructure reveals a surprising result: reducing working memory by 87.5\% (from 2048 to 256 tokens) incurs negligible performance degradation on multi-step reasoning tasks while maintaining constant VRAM usage of 14.8GB. Our findings suggest that current foundation models exhibit remarkable redundancy in their attention patterns, opening new avenues for memory-efficient agentic architectures.

Submission Number: 101