LLM Inference Baseline Testing: Fundamental Methodology for Building Scalable Inference Systems

This project focuses on core issues in building LLM inference systems, emphasizing the importance of establishing a reliable inference baseline before introducing complex architectures. Using vLLM as the testing platform, through characterization analysis of a single backend under real workloads, it provides a reference standard for subsequent optimizations, helps identify performance bottlenecks, and guides the design of intelligent scheduling strategies. The core philosophy is "understand first, optimize later", avoiding directional deviations caused by premature deployment of advanced features such as load balancing.

In the process of building LLM inference systems, a common mistake is to prematurely introduce multi-layer architectures (such as load balancing, intelligent routing) while ignoring in-depth understanding of the performance of the underlying inference backend. This approach may mask real performance bottlenecks, leading to deviations in optimization directions. This project aims to solve this problem through a systematic methodology, laying the foundation for scalable inference systems.

The core design philosophy of the project is "understand first, optimize later", and vLLM is chosen as the testing platform (the de facto standard for high-performance LLM inference in the open-source community). Testing scenarios cover typical workloads: diversity in input length distribution (from short questions to long documents), request arrival patterns (Poisson, burst, time-varying loads), and output length uncertainty. The performance indicator system includes: time decomposition (queuing/computation/transmission latency), resource utilization (GPU computation/video memory bandwidth/capacity), and quality of service (output consistency).

Key experimental findings: 1. The PagedAttention mechanism of vLLM has memory fragmentation issues when there are large differences in output lengths; 2. Continuous batching improves throughput compared to static batching, but it depends on the workload (the advantage is not obvious in low request rate scenarios); 3. The choice of GPU model is critical—newer generation GPUs are better in energy efficiency ratio and memory capacity, and in long-context tasks, video memory capacity is more important than computation speed.

Implications of baseline testing for architectural design: 1. Routing layer: A simple round-robin strategy is sufficient in most scenarios; complex load routing only yields significant benefits under specific conditions; 2. Multi-backend deployment: Homogeneous configuration is recommended to avoid load imbalance caused by mixing GPU models; only long-context tasks should consider high-memory backends; 3. Auto-scaling: Achieve precise planning through mapping between request arrival rate and capacity, and determine scaling thresholds and cooling periods.

Methodology transferability: Although vLLM is used as the platform, the process is applicable to other engines such as TensorRT-LLM and DeepSpeed Inference—the key is a consistent measurement framework. Future research directions: Explore heterogeneous hardware (CPU+GPU) collaborative optimization, research on inference characteristics of multimodal models, and development of adaptive batching strategies.