Developer Resources For Financial Services

A hub of news, SDKs, technical resources, and more for developers working in the financial services industry.

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Build With NVIDIA AI Enterprise

NVIDIA AI Enterprise is an end-to-end, cloud-native software platform that accelerates data science pipelines and streamlines development and deployment of production-grade AI applications, including generative AI. Explore the tools that it offers for financial services.

Microservices and SDKs

Build and Deploy Generative AI

A collage images related to NVIDIA NIM, a set of generative AI microservices for developers


NVIDIA NIM, part of NVIDIA AI Enterprise, is a set of easy-to-use microservices designed to accelerate the deployment of generative AI models across the cloud, data center, and workstations. Built on robust foundations, including inference engines like NVIDIA Triton™ Inference Server, NVIDIA® TensorRT™, NVIDIA TensorRT-LLM, and PyTorch, NIM is engineered to facilitate seamless AI inferencing at scale.

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NVIDIA NeMo framework


NVIDIA NeMo™ is an end-to-end platform for developing custom generative AI—including large language models (LLMs), multimodal, vision, and speech AI —anywhere. Deliver enterprise-ready models with precise data curation, cutting-edge customization, retrieval-augmented generation (RAG), and accelerated performance.

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Accelerate AI With NVIDIA AI Enterprise and CUDA-X

NVIDIA CUDA-X™, built on top of CUDA®, is a collection of microservices, libraries, tools, and technologies for building applications that deliver dramatically higher performance than alternatives across data processing, AI, and high-performance computing (HPC).

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NVIDIA Triton Inference Serve

NVIDIA Triton Inference Server is open-source software that standardizes AI model deployment and execution across every workload.

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NVIDIA Riva is a set of GPU-accelerated multilingual speech and translation microservices for building fully customizable, real-time conversational AI pipelines.

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NVIDIA RAPIDS™ is an open-source suite of GPU-accelerated data science libraries with simple integration options for the most popular data science tools.

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The NVIDIA HPC SDK includes the proven compilers, libraries, and software tools essential to maximizing developer productivity and the performance and portability of HPC applications.

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Graph neural network (GNN) frameworks are easy-to-use Python packages that offer building blocks to build GNNs on top of existing deep learning frameworks for a wide range of applications.

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The NVIDIA CUDA Deep Neural Network (cuDNN) library is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.

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RAPIDS Accelerator for Apache Spark

The RAPIDS Accelerator for Apache Spark leverages GPUs to accelerate processing by combining the power of the RAPIDS cuDF library and the scale of the Spark distributed computing framework.

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NVIDIA Morpheus

NVIDIA Morpheus is a GPU-accelerated, end-to-end AI framework that enables developers to create optimized applications for filtering, processing, and classifying large volumes of streaming cybersecurity data.

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How to Accelerate Quantitative Finance With ISO C++ Standard Parallelism

In this blog post, we demonstrate how to use quantitative finance libraries to support the valuation, risk management, construction, and optimization of investment portfolios.

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An end-to-end workflow for generative AI and data science

Accelerating Inference on End-to-End Workflows With and NVIDIA

Data scientists are combining generative AI and predictive analytics to build the next generation of AI applications in financial services.

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 NVIDIA H100 Tensor Core GPU for accelerated computing

New Risk Calculation Record in Financial Services With Dell Technologies and NVIDIA H100 System for HPC and AI

Dell and NVIDIA covered quantitative applications setting new records on Dell servers with NVIDIA GPUs, independently audited by STAC on financial quantitative HPC workloads.

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A customer make purchases with credit card on a mobile phone

Predicting Credit Defaults Using Time-Series Models With Recursive Neural Networks and XGBoost

In this post, we demonstrate how NVIDIA RAPIDS supports data preparation and ML training for large datasets and how NVIDIA Triton Inference Server seamlessly serves both deep neural nets by PyTorch and tree-based models by XGBoost when predicting credit default.

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Accelerating End-to-End Data Science Workflows

In this self-paced lab, learn how to build and execute end-to-end, GPU-accelerated data science workflows that let you quickly explore, iterate, and get your work into production. Using the RAPIDS accelerated data science libraries, you'll learn how to apply a wide variety of GPU-accelerated machine learning algorithms to perform data analysis at scale.

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Digital Fingerprinting With NVIDIA Morpheus

In this free self-paced course, get hands-on experience developing and deploying the NVIDIA Morpheus digital fingerprinting AI workflow, which enables 100 percent data visibility and drastically reduces the time to detect threats.

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Building Conversational AI Applications

In this instructor-led, hands-on workshop, learn how to quickly build and deploy production-quality conversational AI applications with real-time transcription and natural language processing capabilities using the NVIDIA Riva framework.

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Developer Resources

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