Practical Evaluation of LLM Inference Performance: In-depth Comparative Analysis Between vLLM and HuggingFace Transformers

This project was published by tochikoma777 on GitHub (original link: https://github.com/tochikoma777/llm-inference-benchmark). Based on the NVIDIA RTX 3090 graphics card and Qwen2.5-7B model, it systematically compares the performance differences between the two major inference frameworks, vLLM and HuggingFace Transformers, aiming to provide data support for LLM deployment in production environments.

Inference costs account for over 70% of the total cost in some AI services, and optimization strategies vary significantly between frameworks: HuggingFace Transformers provides standard processes but no in-depth optimization, while vLLM introduces PagedAttention optimized for high throughput. Lack of systematic evaluation can easily lead to one-sided technology selection decisions, causing performance bottlenecks or resource waste in production environments.

The test hardware is NVIDIA RTX 3090 (24GB VRAM), and the model used is Qwen2.5-7B. Comparative frameworks:

• HuggingFace Transformers: Widely used in the community, mature ecosystem, standardized interfaces, but no in-depth inference performance optimization by default;
• vLLM: Developed by the Berkeley team, core innovation is the PagedAttention technology, which improves GPU memory utilization efficiency to support high concurrent throughput.

vLLM improves KV Cache memory management through PagedAttention:

1. Split KV Cache into fixed-size blocks, dynamically map logical blocks to physical blocks via a block table to reduce memory fragmentation;
2. Support storing KV Cache of multiple sequences in the same physical block, share blocks for identical prefixes, and use copy-on-write mechanism when they differ, leading to significant performance improvements in high-concurrency scenarios.

1. Latency: vLLM's continuous batching technology reduces average waiting time and improves the smoothness of interactive applications;
2. Throughput: PagedAttention improves memory efficiency, supports larger batch sizes, and has obvious advantages in server-side throughput;
3. Memory Efficiency: vLLM usually supports 2-4 times more concurrent requests under the same memory, making it suitable for memory-constrained environments.

• Advantages of HuggingFace: Mature ecosystem, fast model updates, rich fine-tuning/quantization tools;
• Limitations of vLLM: Multi-card parallelism needs a multi-machine environment to realize its value, and model architecture support is not as comprehensive as Transformers;
• Compatibility: Some custom models may need to be adapted to vLLM source code.

1. Prioritize vLLM for high-throughput server-side deployment;
2. vLLM's memory efficiency lowers hardware thresholds (RTX3090 can run Qwen2.5-7B smoothly);
3. Establishing a reproducible benchmarking process is the foundation of data-driven decision-making.

vLLM has verified the practical effects of advanced optimization technologies, providing a reliable basis for technology selection. Future inference optimization will develop towards fine-grained resource scheduling, intelligent batching, and flexible quantization. Practitioners should pay attention to benchmarking and establish systematic evaluation capabilities to support decision-making for LLM application implementation.