Saving GPU Hours in LLM Inference System Development and Online Workloads with Simulation and DBMS-Inspired Cache Replacement Policies
arXiv:2411.07447v5 Announce Type: replace-cross Abstract: LLMs are increasingly used world-wide from daily tasks to agentic systems and data analytics, requiring significant GPU resources. While LLM inference systems are capable of serving millions of requests from multiple users, they often lack theoretical models to determine whether they achieve the performance upper bounds of underlying hardware resources. Beyond online workload serving, merely analyzing existing systems-or developing yet another one-is both GPU-intensive and labor-intensive. This paper provides a comprehensive survey of L
The proliferation of LLMs across diverse applications has made GPU resource optimization critical, especially as current systems often lack theoretical models for performance upper bounds.
Reducing GPU hours for LLM inference system development and online workloads directly addresses a significant cost and scalability constraint in the booming AI industry.
The proposed simulation and DBMS-inspired cache replacement policies offer a new methodology for optimizing LLM inference, potentially leading to more efficient resource utilization and lower operational costs.
- · AI software developers
- · Cloud providers
- · LLM operators
- · Data centers
- · Inefficient LLM inference providers
- · GPU manufacturers (indirectly, if efficiency reduces demand for *more* hardware)
More cost-effective deployment and development of large language models.
Accelerated innovation in AI applications due to reduced compute barriers.
Potentially democratized access to advanced AI capabilities by lowering infrastructure costs.
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Read at arXiv cs.AI