How NVIDIA Uses Git
30 engineering articles about Git from NVIDIA's engineering team
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Deploying an AI coding assistant in a regulated, sovereign, or source-sensitive environment, often comes with challenges. Three common issues are: the source…
Tanya Lenz
13 min read
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AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on…
Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can…
Boris Ivanovic
8 min read
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Creative and visualization teams today produce more assets, in more formats, with leaner teams. Generative AI can accelerate that work – compressing tasks that…
Joel Pennington
10 min read
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Building AI factories is complex and requires efficient integration across compute, networking, security, and storage systems. To achieve rapid Time to AI and…
The article provides practical security guidance for sandboxing agentic workflows, emphasizing the importance of managing execution risk associated with AI coding agents.
The article discusses the deployment of secure, data-driven AI agents using NVIDIA's AI-Q Research Assistant and Enterprise RAG Blueprints on AWS.
Abdullahi Olaoye
8 min read
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The article discusses how Continuous Integration and Continuous Delivery/Deployment (CI/CD) practices can be applied to network automation, particularly with Cumulus Linux and the NVIDIA Air digita...
The article discusses how NVIDIA Air facilitates network automation using tools like Ansible and Git, emphasizing the importance of coding, versioning, and automating network configurations.
The article discusses the operational challenges of deploying large language models (LLMs) and introduces LLMOps as a framework for managing their lifecycle.
Liad Levi-Raz
12 min read
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The article discusses the advantages of using external file uploads for creating scalable and customizable network topologies in NVIDIA Air.
The article discusses the transition of AI from centralized cloud systems to local development on professional workstations, emphasizing the advantages of enhanced data privacy, cost savings, and o...
The article introduces NVIDIA RTX Kit, a suite of neural rendering technologies that enhances performance and image quality in computer graphics.
The article discusses the NVIDIA AI Workbench, a free development environment manager designed for developing, customizing, and prototyping AI applications across local and cloud systems.
The article discusses the NVIDIA AI Workbench, a free development environment manager designed to facilitate frictionless collaboration and rapid prototyping in hybrid environments for data science...
The article discusses the integration of large language models (LLMs) into enterprise applications using NVIDIA NIM and Outerbounds, emphasizing the importance of secure deployment, continuous impr...
Ville Tuulos
14 min read
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NVIDIA AI Workbench is a free development environment manager that simplifies the use of GPUs on Windows, macOS, and Ubuntu for data science, machine learning, and AI projects.
The article discusses the deployment of multilingual large language models (LLMs) using NVIDIA NIM, highlighting the importance of effective communication across languages in a globalized business ...
Amit Bleiweiss
9 min read
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NVIDIA AI Workbench is a newly available toolkit designed to streamline AI and ML development for both novice and expert developers.
André Franklin
4 min read
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The article discusses StarCoder2, an advanced large language model (LLM) designed to enhance coding efficiency for developers.
Chia-Chih Chen
7 min read
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The article provides a comprehensive guide on deploying an AI coding assistant using NVIDIA TensorRT-LLM and NVIDIA Triton.
Amit Bleiweiss
12 min read
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NVIDIA AI Workbench is now in beta, offering features that simplify the creation, sharing, and scaling of AI and machine learning workflows for enterprise developers.
Shruthii Sathyanarayanan
10 min read
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The article discusses the NVIDIA AI Workbench, a unified toolkit designed to simplify the development and deployment of scalable generative AI models.
Tyler Whitehouse
10 min read
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This article explores how to perform large-scale graph analytics using Memgraph and NVIDIA cuGraph algorithms, specifically focusing on PageRank and Louvain community detection.
The article discusses how to modernize data center network operations through the NetDevOps ideology, which emphasizes optimizing infrastructure operations.
NVIDIA has released Linux GPU kernel modules as open source under a dual GPL/MIT license, starting with the R515 driver.
Ram Cherukuri
7 min read
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The article introduces NVIDIA Data Science Workbench, a tool designed to streamline the model-building process for data scientists and AI developers by simplifying software management and enhancing...
André Franklin
3 min read
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The article discusses how the NVIDIA RTX Memory Utility (RTXMU) optimizes memory management for acceleration structures in ray tracing applications.
Peter Morley
10 min read
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This article provides a comprehensive guide on utilizing Real-Time Ray Tracing and DLSS in Unreal Engine 4 (UE4), highlighting performance improvements and new features.
Nefi Alarcon
4 min read
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The article discusses how Kubernetes can be leveraged for AI hyperparameter search experiments, highlighting the shift from local to centralized infrastructure for AI workloads.
Shashank Prasanna
20 min read
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