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This article introduces an open-source implementation of a production-grade LLM inference service system, detailing core optimization techniques such as continuous batching, block-based KV cache management, and speculative decoding, and demonstrates how to achieve 3.4x higher throughput than naive batching.
Section 01
This article introduces the open-source project "LLM-Inference-Serving-System". Through three core technologies—continuous batching, block-based KV cache management, and speculative decoding—it achieves 3.4x higher throughput than naive batching in mixed-length request scenarios, addressing inference latency and throughput issues in large model deployment.
Section 02
Large model inference faces computational characteristics of autoregressive generation (computation increases with sequence length, complex memory access, large variation in request lengths, latency sensitivity), while naive batching has issues like padding waste, synchronous waiting, and low GPU utilization.
Section 03
Section 04
The system includes a scheduler (request management), KV cache manager (memory allocation), execution engine (inference computation), and token generator (sampling and conversion); it also optimizes operator fusion, quantization support, pipeline parallelism, asynchronous IO, etc.
Section 05
In mixed-length request scenarios, throughput increased by 3.4x; latency is affected by first-token, per-token, and tail latency; in terms of resource utilization, it maintains high GPU utilization, reduces memory fragmentation, and dynamically adapts to load.
Section 06
Applicable to high-concurrency online services, mixed loads, and cost-sensitive deployments; deployment requires selecting appropriate GPUs, tuning batch size, speculative parameters, and scheduling strategies, monitoring key metrics, and setting up operation and maintenance mechanisms.
Section 07
Inference optimization is as important as training; open-source projects drive technological progress; future directions include multimodal support, edge deployment, adaptive optimization, and heterogeneous computing; this project provides practical references for LLM inference services.
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