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 toolkit and Dynamo-Triton software on NVIDIA Blackwell Ultra GPUs, Revolut is scaling PRAGMA in production, servicing an initial 80 million customers and engineered to scale 3x.
“Revolut processes billions of transactions — the opportunity was always there, but the question was whether foundation models could operate at that scale and across domains. PRAGMA answers that,” said Pavel Nesterov, head of AI at Revolut. “Built in collaboration with NVIDIA, it’s the clearest proof yet that the gap between a research breakthrough and a production banking system can be closed.”
Training on Accelerated Infrastructure
Starling Bank, the first bank to train an agentic model on U.K. sovereign cloud infrastructure, has developed “Starling Intelligence” — the intelligence layer that underpins its AI capability — on Civo’s U.K. cloud using the bank’s own transaction data.
Lloyds Banking Group built ATUM, a transaction foundation model that converts billions of banking transactions into reusable intelligence — accelerated with NVIDIA RTX PRO 6000 Blackwell GPUs and improving efficiency across bank-wide use cases.
SBS launched a banking sovereign intelligence platform so banks can run generative and agentic models without their data leaving their own walls.
Open Models at a Fraction of the Cost
Open weight models give European financial institutions proprietary intelligence built on data they own, at costs they control. NVIDIA’s accelerated computing platform is the engine running beneath them.
Neobank bunq’s agentic assistant Finn now runs on open weight models. Powered by NVIDIA accelerated computing, deploying on Nebius matches the quality of leading closed models at 60% of the cost per answer.
NatWest is building technology designed to recognize the full range of British accents, dialects and financial terminology, developed with NVIDIA’s speech models, including Nemotron 3.5 ASR.
Modulr is building specialized models for distinct parts of payment operations, based on the NVIDIA Nemotron-3-Ultra-550B open model.
Santander is an early adopter of the new NVIDIA Kumo family of open weight foundation models for structured financial data, using them to build specialized intelligence for core banking workloads.
Accelerated Computing Transforms Financial Research
LSEG is working with NVIDIA to help financial institutions analyze large volumes of historical tick data up to 10x faster with the Polars GPU engine, accelerated by NVIDIA cuDF on NVIDIA RTX PRO 6000 Blackwell GPUs.
RavenPack built a dataset generation pipeline on Bigdata.com that takes a plain-language research hypothesis and returns a labeled, backtest-ready dataset — running on a fine-tuned NVIDIA Nemotron model. Investors can use these datasets to identify, analyze and visualize market trends.
Speeding Data Pipelines to Improve Fraud Detection
RTX PRO 6000 Blackwell Server Edition GPUs and NVIDIA cuDF help accelerate data processing across multi-terabyte-scale, siloed, regulated sources, compressing the time from data to decision for fraud and other scenarios.
HSBC provides near-real-time client insights that support customer processes and on-demand decision-making while minimizing risk and fraud, powered by 5x faster data processing on Starburst Enterprise’s GPU Engine.
Stripe, a global provider of programmable financial services, sees 42x faster data pipelines and 92% cost savings for its Stripe Radar fraud-detection platform with NVIDIA GPU-accelerated Ray Data in offline testing. This enables Stripe Radar’s machine learning pipelines to continuously optimize model training and improve fraud detection.
Learn more about NVIDIA accelerated computing in financial services.
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