How Revolut Cut AI Coding Costs 90% With Self-Hosted Open Models - David Abitbol, Revolut

Summary

David Abitbol de Revolut explica cómo la empresa optimizó sus costos de IA en un 90% al migrar hacia modelos de código abierto autoalojados en sus propias GPUs. Tras notar que servicios como Claude o Gemini no siempre ofrecían el retorno de inversión esperado y suponían costos elevados, Revolut implementó una infraestructura propia basada en modelos GLM. La estrategia incluye el uso de sandboxes para garantizar supervisión humana, optimización técnica de parámetros como la cuantización FP4 para maximizar el rendimiento, y la gestión de la demanda mediante una infraestructura compatible con OpenAI. Esto permitió a sus ingenieros trabajar con tokens ilimitados sin restricciones presupuestarias, mejorando drásticamente los tiempos de entrega y desarrollo.

Topics covered

• Estrategia de adopción de IA en Revolut
• Impacto de modelos open source en productividad

+5 more topics (full content when you follow the channel)

Transcript excerpt

[music] >> My name is David. I lead AI operations at a company called Revolut. We have 50 minutes today together. We'll talk about high-performance engineering and self-hosting open-source model on dedicated GPU. How's that sound? Cool. So, it's going to be a technical one, so bear with me. Um so, maybe to start off because I know this is the US and you may not know Revolut. Uh quick words about our company. We're a digital bank in Europe. We have 80 million customers. We're live in 45 markets. With the most downloaded financial app in Europe for the last year. And as of July, we're Europe's largest private company. Um So, we're doing okay. The the emphasis of Revolut is, you know, shipping a lot of product and so there's a high focus on engineering culture and uh excellence on that domain. And so, we'll talk about that. So, let's talk about some AI. We have a chart here. I going to see…