arXiv:2603.26557v2 Announce Type: replace Abstract: Large Language Models (LLMs) deliver strong performance but incur high inference cost in real-world services, especially under workloads with repeated or near-duplicate queries across users and sessions. In this work, we propose MemBoost, a memory-boosted LLM serving framework that enables a lightweight model to reuse previously generated answers and retrieve relevant supporting information for cheap inference, while selectively escalating difficult or uncertain queries to a stronger model. Unlike standard retrieval-augmented generation, whic
The proliferation of LLMs in real-world services is driving a critical need to optimize inference costs and efficiency, especially with repeated queries.
This development addresses a major bottleneck for ubiquitous LLM deployment, potentially making advanced AI more accessible and economically viable for a wider range of applications.
The economics of LLM inference could significantly improve, allowing for more cost-effective scaling and broader integration of powerful AI models into various products and services.
- · LLM service providers
- · Generative AI application developers
- · Cloud infrastructure providers (optimizing LLM workloads)
- · Enterprises adopting AI
- · Inefficient LLM architectures
- · Companies relying on brute-force compute scaling without optimization
Reduced operational costs for AI products leveraging LLMs.
Accelerated adoption of LLMs across industries due to improved cost-efficiency.
Increased competition among AI service providers focusing on optimized inference, potentially leading to 'commodity' LLM services.
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Read at arXiv cs.CL