Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems

Analysis

Why This Paper Matters

Generative large language models (LLMs) have revolutionized human-AI interaction, but their deployment is hampered by substantial computational and memory demands. Efficient serving is critical for enabling low-latency, high-throughput applications, yet the landscape of optimization techniques is fragmented across algorithmic and system-level research. This survey, published in ACM Computing Surveys, addresses this gap by providing a holistic view from a machine learning systems (MLSys) perspective. It stands at the intersection of AI innovation and practical system optimization, offering a timely synthesis for researchers and practitioners.

The paper's significance lies in its comprehensive coverage of solutions ranging from algorithmic modifications to system design changes. By bridging these domains, it helps readers understand the trade-offs and synergies between different approaches. As LLMs continue to grow in size and complexity, such integrated perspectives are essential for overcoming deployment barriers and reshaping the future of AI applications.

Technical Contributions

• Comprehensive Taxonomy: The survey organizes efficient LLM serving techniques into a coherent framework, distinguishing between algorithmic optimizations (e.g., quantization, pruning, efficient attention) and system-level designs (e.g., memory management, scheduling, hardware acceleration).
• MLSys Perspective: It emphasizes the importance of co-designing algorithms and systems, highlighting how cross-layer optimizations can yield significant efficiency gains.
• In-depth Analysis: The authors provide detailed discussions of representative methods, their assumptions, and their applicability in different serving scenarios.
• Future Directions: The survey identifies open challenges and promising research directions, guiding future work in the field.

Results

As a survey paper, it does not present new experimental results. Instead, it synthesizes findings from a broad range of prior studies, offering qualitative comparisons and insights. The paper discusses performance metrics such as latency, throughput, and memory footprint, and analyzes how different techniques affect these metrics. It serves as a meta-analysis of the state of the art, enabling readers to understand the relative strengths and weaknesses of various approaches without conducting new experiments.

Significance

This survey is significant for both academia and industry. For researchers, it provides a structured overview of the field, highlighting gaps and opportunities for innovation. For practitioners, it offers guidance on selecting and combining techniques for efficient LLM deployment. By emphasizing the MLSys perspective, the paper encourages cross-disciplinary collaboration and the development of integrated solutions. Ultimately, it contributes to the broader goal of making generative AI more accessible, sustainable, and scalable, thereby accelerating the adoption of LLMs in real-world applications.