LLM Inference Brief: vLLM vs TGI Throughput Economics

Frequently Asked Questions

01.What is LLM inference optimisation?

LLM inference optimisation is the set of software and systems techniques used to serve large language model predictions faster and at lower cost per token, including memory management for the attention mechanism, request batching, and quantisation. It matters commercially because inference, not training, is the recurring cost line that determines whether an AI product is sustainable at scale.

02.How does vLLM differ from Hugging Face Text Generation Inference?

vLLM is a serving engine built around PagedAttention, a memory management technique that reduces wasted GPU memory in the key-value cache, while Text Generation Inference is a production toolkit from Hugging Face offering continuous batching, tensor parallelism, and multiple quantisation formats under an Apache-2.0 license. Both target the same cost problem using overlapping but distinct engineering approaches.

03.Who should care about inference infrastructure economics?

Capital allocators funding AI-native products, infrastructure vendors selling GPU capacity, and enterprise buyers signing multi-year inference contracts should all track this economics closely, because a documented 80 percent of raised capital at AI-native companies goes toward compute, making serving efficiency a direct driver of runway and margin.