MemBoost: A Memory-Boosted Framework for Cost-Aware LLM Inference

2026-06-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, medium

Summary

MemBoost is a novel memory-boosted LLM serving framework designed to significantly reduce the high inference costs associated with large language models, particularly for workloads involving repeated or near-duplicate queries. Unlike traditional retrieval-augmented generation (RAG) systems that focus on single-response grounding, MemBoost supports interactive settings through answer reuse, continuous memory growth, and cost-aware routing. The framework comprises three key components: an Associative Memory Engine (AME) for fast semantic retrieval and storing past answers, a high-capability Large-LLM Oracle for accurate fallback on complex queries, and a lightweight Meta Controller (MC) that orchestrates query handling, deciding whether to use AME or escalate to the Oracle, and managing write-back of new high-quality answers. Experiments under simulated workloads, including the MMLU-Pro dataset, demonstrate that MemBoost substantially decreases expensive large-model invocations while maintaining answer quality comparable to the strong model baseline.

Key takeaway

For MLOps Engineers managing LLM deployments, MemBoost offers a clear strategy to significantly reduce operational costs without sacrificing quality. If your LLM deployment incurs high GPU expenses from repeated queries, implement a tiered architecture with semantic caching and dynamic memory updates. This approach allows your system to efficiently reuse answers and intelligently route complex requests, optimizing resource utilization and improving cost-effectiveness.

Key insights

MemBoost reduces LLM inference costs by intelligently reusing answers and routing complex queries to a powerful oracle.

Principles

• Prioritize memory reuse for common queries.
• Escalate complex queries to a stronger model.
• Continuously update memory with new high-quality answers.

Method

The system retrieves from an Associative Memory Engine (AME), then a Meta Controller (MC) decides to answer from memory or escalate to a Large-LLM Oracle, optionally writing back new answers.

In practice

• Implement semantic caching for LLM responses.
• Design a tiered LLM architecture.
• Integrate dynamic memory write-back.

Topics

• LLM Inference Cost Optimization
• Retrieval-Augmented Generation
• Semantic Caching
• Multi-Model LLM Routing
• Associative Memory Engine
• MMLU-Pro Dataset

Best for: Machine Learning Engineer, NLP Engineer, CTO, AI Engineer, MLOps Engineer, AI Architect

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• VikingMem: A Memory Base Management System for Stateful LLM-based Applications — VikingMem introduces a Memory Base paradigm for stateful LLM interactions, improving memory retrieval and generalizability.
• Prism: Cost-Efficient Multi-LLM Serving via GPU Memory Ballooning — Dynamic, demand-aware cross-model memory coordination is crucial for cost-efficient multi-LLM serving with strict SLOs.
• The Complete Guide to Inference Caching in LLMs — Inference caching optimizes LLM performance and cost by reusing computation results across three distinct layers.
• MeMo's memory model lets teams upgrade their LLM without retraining it — and performance jumps 26% — MeMo uses a separate, smaller memory model to update LLM knowledge without retraining the main reasoning engine.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.