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## Build a self-hosted, GPU-accelerated translation API with open-source LLMs

DevFeed: [Build a self-hosted, GPU-accelerated translation API with open-source LLMs](<https://devfeed.tech/articles/build-a-low-cost-low-latency-low-energy-ai-translator-a-deepl-alternative-that-works-23896.md>)

Original publisher: [Read original article](<https://medium.com/smg-real-estate/build-a-low-cost-low-latency-low-energy-ai-translator-a-deepl-alternative-that-works-19b903d00845?source=rss----2186e5b9bd8f---4>)

Author: fm.mateo

Published: 2025-08-07T12:57:31Z

Content type: tutorial

Language: en

Sources: [Homegate Engineering Blog - Medium](<https://devfeed.tech/sources/homegate-engineering-blog-medium.md>)

Topics: [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Google Cloud Platform (GCP)](<https://devfeed.tech/topics/google-cloud.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Self-hosted](<https://devfeed.tech/topics/self-hosted.md>), [API](<https://devfeed.tech/topics/api.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [Tooling](<https://devfeed.tech/topics/tooling.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [amazon](<https://devfeed.tech/tags/amazon.md>), [api](<https://devfeed.tech/tags/api.md>), [backend](<https://devfeed.tech/tags/backend.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cost](<https://devfeed.tech/tags/cost.md>), [deepl](<https://devfeed.tech/tags/deepl.md>), [energy](<https://devfeed.tech/tags/energy.md>), [google-translate](<https://devfeed.tech/tags/google-translate.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [machine-translation](<https://devfeed.tech/tags/machine-translation.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [tooling](<https://devfeed.tech/tags/tooling.md>), [translation](<https://devfeed.tech/tags/translation.md>)

### AI overview

This step-by-step guide describes how Swiss Marketplace Group built and deployed a self-hosted, GPU-accelerated translation service using open-source large language models on Google Cloud Platform. The article reports over 85% cost savings, sub-three-second latency, and no impact on buyers' conversion rate compared with its previous DeepL API usage.

### Source excerpt

Build a low-cost, low-latency, low-energy AI Translator: a DeepL alternative that worksA step-by-step guide to deploying your own AI-powered translation API at scale, and get rid of DeepL, Google Translate, Amazon Translate, etc.Introduction and motivation At Swiss Marketplace Group (SMG), we operate multiple online platforms used daily by millions of people across Switzerland. One of them, Ricardo, is a second-hand marketplace where buyers and sellers interact in four different languages: French, German, Italian, and English. To deliver a seamless experience across languages, we had been relying on DeepL API, one of the most accurate translation APIs on the market. And while DeepL served us well in terms of quality and response time, it came with significant downsides: A monthly cost of over 7,000 CHF A black-box approach, offering no insight into how translations were produced Data privacy risks As usage grew, so did the bill, and the urgency to reduce it. Around that time, generative AI had made enormous improvements, and open-source large language models (LLMs) had become more accessible than ever. This sparked a question: Can we replace DeepL API with a self-hosted open-source LLM, without sacrificing translation quality, speed, and user experience? The answer turned out to be yes. Over the course of my master thesis, I built a production-grade, GPU-accelerated translation service using modern LLM tooling, hosted entirely on Google Cloud Platform (GCP), and fully integrated into Ricardo's infrastructure. The result: Over 85% cost savings Sub-3-second latency No impact on conversion rate of buyers And everything is reproducible, transparent, and extensible. In this article, I'll walk you through exactly how I built it, from selecting the right model, optimizing inference time, and wrapping it in an API, to deploying and monitoring it in production. My goal is simple: enable you to do the same. Whether you're a backend developer, a ML engineer, or just AI-curious