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European finance firms show NVIDIA-backed models at GTC Berlin

Original title: Leading European Financial Services Organizations Embrace Open Models to Build Proprietary Intelligence

NVIDIA Blogby Jason Renders ()3 min read2 · 1 read
Part of a story covered by 6 sourcesNvidia ships Nemotron 3.5 models for enterprise and local agentsMerged summary & timeline
AI summary · x-ai/grok-4.7Summaries by Vani AI

Revolut says PRAGMA starts with 80 million customers, and bunq claims 60% of closed-model cost per answer.

At GTC Berlin, NVIDIA described European banks and payment firms running open-weight and proprietary models on its accelerated computing. Revolut is putting foundation model PRAGMA into production, Starling Bank trained Starling Intelligence on Civo’s U.K. cloud, and Lloyds built transaction model ATUM. Other named users include bunq, NatWest, Modulr, Santander, LSEG, HSBC, and Stripe. Performance and cost figures are company or NVIDIA claims.

Unconfirmed: Speed, scale, and cost figures are vendor or customer claims, not independently verified.

Key facts
  • bunq’s Finn assistant is said to match closed models at 60% of the cost per answer.
  • NatWest is using NVIDIA speech models, including Nemotron 3.5 ASR.
  • Modulr is basing payment models on Nemotron-3-Ultra-550B.
  • Stripe reports 42x faster pipelines and 92% cost savings for Radar in offline testing.
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From the original article

3 min read at blogs.nvidia.com

At GTC Berlin, banks, payment companies and financial data providers are showing what production-scale intelligence looks like — built on NVIDIA accelerated computing, powered by open weight models and running on their own infrastructure. Revolut is rebuilding its technology stack on a single foundation model PRAGMA, powered by NVIDIA accelerated computing. Using the NVIDIA cuDF […]

NVIDIA Blog

By Jason Renders · blogs.nvidia.com

Read the full article at NVIDIA Blog

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.