vLLM vs. TGI: The Ultimate Comparison for Speed, Scalability, and LLM Performance

Introduction

This blog compares two popular inference libraries: vLLM and Text Generation Inference (TGI). These tools optimize LLM deployment and execution for speed and efficiency.

vLLM, developed at UC Berkeley, introduces PagedAttention and Continuous Batching to improve inference speed and memory usage. It supports distributed inference across multiple GPUs.

TGI, created by Hugging Face, is a production-ready library for high-performance text generation. It offers a simple API and compatibility with various models from the Hugging Face hub.

Comparison Analysis

Performance Metrics

vLLM and TGI are popular choices for serving large language models (LLMs) due to their efficiency and performance. Let's compare them based on latency, throughput, and time to first token (TTFT):

Features

‍Both vLLM and Text Generation Inference (TGI) offer robust capabilities for serving large language models efficiently. Below is a detailed comparison of their features.

Ease of Use

Scalability

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Integration

Conclusion

vLLM and TGI are both esteemed inference libraries optimized for serving LLMs efficiently. vLLM typically excels in terms of throughput, latency, and TTFT, particularly under conditions of high concurrency. Additionally, it offers enhanced flexibility with LoRA adapters. In contrast, TGI specializes in streamlining inference for text generation models, primarily within the Hugging Face ecosystem.

The decision to choose between vLLM and TGI often hinges on specific use cases, deployment environments, and the particular models being served. Both libraries are under continuous development, rapidly adapting to meet user demands and broadening their range of capabilities.

Resources

1. https://www.inferless.com/learn/exploring-llms-speed-benchmarks-independent-analysis---part-3
2. https://www.inferless.com/learn/exploring-llms-speed-benchmarks-independent-analysis---part-2
3. https://www.inferless.com/learn/exploring-llms-speed-benchmarks-independent-analysis
4. https://pages.run.ai/hubfs/PDFs/Serving-Large-Language-Models-Run-ai-Benchmarking-Study.pdf
5. https://huggingface.co/docs/text-generation-inference/index
6. https://huggingface.co/blog/martinigoyanes/llm-inference-at-scale-with-tgi
7. https://medium.com/cj-express-tech-tildi/how-does-vllm-optimize-the-llm-serving-system-d3713009fb73
8. https://docs.vllm.ai/en/latest/
9. https://www.bentoml.com/blog/benchmarking-llm-inference-backends
10. https://arxiv.org/html/2401.08671v1
11. https://www.anyscale.com/blog/continuous-batching-llm-inference

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