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Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
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Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
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In the rapidly evolving landscape of artificial intelligence (AI), generative large language models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However, the computational intensity and memory consumption of deploying these models present substantial challenges in terms of serving efficiency, particularly in scenarios demanding low latency and high throughput. This survey addresses the imperative need for efficient LLM serving methodologies from a machine learning system (MLSys) research perspective, standing at the crux of advanced AI innovations and practical system optimizations. We provide in-depth analysis, covering a spectrum of solutions, ranging from cutting-edge algorithmic modifications to groundbreaking changes in system designs. The survey aims to provide a comprehensive understanding of the current state and future directions in efficient LLM serving, offering valuable insights for researchers and practitioners in overcoming the barriers of effective LLM deployment, thereby reshaping the future of AI.
Forward citations
Cited by 7 Pith papers
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BatchGen uses a coroutine-based model to dynamically reorganize inference work across GPUs, achieving up to 2.3x faster batch completion on 128 GPUs and 9.6x improvement on memory-constrained hardware.
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BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models
BPDQ creates variable quantization grids from bit-planes and scalar coefficients, refined iteratively with second-order data to minimize output error, enabling 2-bit serving of Qwen2.5-72B on one RTX 3090 at 83.85% GS...
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ODMA: On-Demand Memory Allocation Strategy for LLM Serving on LPDDR-Class Accelerators
ODMA raises KV-cache utilization by up to 19.25% and throughput by 23-27% on Cambricon MLU accelerators by dynamically adjusting prediction buckets and using a safety pool for LLM serving.
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Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization
A survey organizing serving-time KV cache optimization techniques into temporal, spatial, and structural system behaviors, analyzing cross-behavior co-design patterns and open challenges.
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A Survey on Efficient Inference for Large Language Models
The paper surveys techniques to speed up and reduce the resource needs of LLM inference, organized by data-level, model-level, and system-level changes, with comparative experiments on representative methods.
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A Survey on the Memory Mechanism of Large Language Model based Agents
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
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