Studying quantization trade-offs for efficient inference deployment in machine translation
TL;DR
The experiments show that the trade-off between inference efficiency and translation quality depends not only on the quantization format, but also on the choice of text chunking strategy, as well as on the choice of text chunking strategy.
Abstract
Deploying large language models in realistic server environments poses challenges, as the system needs to provide high-quality responses with low latency. Quantization is a common approach to reduce the memory footprint and improve inference efficiency, yet its impact on latency and throughput is rarely evaluated under controlled, orchestration-level workloads. In this work we study the quantization trade-offs of EuroLLM \citep{martins2025eurollm} across three model sizes ranging from 1.7B to 22B for efficient deployment on a single A100 or H100 GPU. We demonstrate that combining a document-chunking strategy with W4A8 or W8A8 quantization improves the latency-throughput Pareto-curve under a wide range of workloads. Furthermore, since standard machine translation (MT) benchmarks rely on isolated sentences and fail to capture long-context dynamics, we introduce a document-level evaluation based on DocHPLT \cite{o2025dochplt} to assess how text chunking strategies affect translation quality under quantization. Our results indicate that standard segment-level evaluation can potentially underestimate the interaction between quantization and long-context document translation, for some quantization formats, translation direction and models. Overall, our experiments show that the trade-off between inference efficiency and translation quality depends not only on the quantization format, but also on the choice of text chunking strategy.
