Category: Misc

AI agents are emerging as the newest way for organizations to increase efficiency, improve productivity, and accelerate innovation. These agents are more…

AI agents are emerging as the newest way for organizations to increase efficiency, improve productivity, and accelerate innovation. These agents are more advanced than prior AI applications, with the ability to autonomously reason through tasks, call out to other tools, and incorporate both enterprise data and employee knowledge to produce valuable business outcomes. They’re being embedded into…

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AI agents are emerging as the newest way for organizations to increase efficiency, improve productivity, and accelerate innovation. These agents are more…

AI agents are emerging as the newest way for organizations to increase efficiency, improve productivity, and accelerate innovation. These agents are more advanced than prior AI applications, with the ability to autonomously reason through tasks, call out to other tools, and incorporate both enterprise data and employee knowledge to produce valuable business outcomes. They’re being embedded into…

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One challenge organizations face when customizing large language models (LLMs) is the need to run multiple experiments, which produces only one useful model….

One challenge organizations face when customizing large language models (LLMs) is the need to run multiple experiments, which produces only one useful model. While the cost of experimentation is typically low, and the results well worth the effort, this experimentation process does involve “wasted” resources, such as compute assets spent without their product being utilized…

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Learn how to build scalable data processing pipelines to create high-quality datasets.

Learn how to build scalable data processing pipelines to create high-quality datasets.

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Deploying large language models (LLMs) in production environments often requires making hard trade-offs between enhancing user interactivity and increasing…

Deploying large language models (LLMs) in production environments often requires making hard trade-offs between enhancing user interactivity and increasing system throughput. While enhancing user interactivity requires minimizing time to first token (TTFT), increasing throughput requires increasing tokens per second. Improving one aspect often results in the decline of the other…

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Fraud in financial services is a massive problem. According to NASDAQ, in 2023, banks faced $442 billion in projected losses from payments, checks, and credit…

Fraud in financial services is a massive problem. According to NASDAQ, in 2023, banks faced $442 billion in projected losses from payments, checks, and credit card fraud. It’s not just about the money, though. Fraud can tarnish a company’s reputation and frustrate customers when legitimate purchases are blocked. This is called a false positive. Unfortunately, these errors happen more often than…

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The rapid development of solutions using retrieval augmented generation (RAG) for question-and-answer LLM workflows has led to new types of system…

The rapid development of solutions using retrieval augmented generation (RAG) for question-and-answer LLM workflows has led to new types of system architectures. Our work at NVIDIA using AI for internal operations has led to several important findings for finding alignment between system capabilities and user expectations. We found that regardless of the intended scope or use case…

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NVIDIA today announced that xAI’s Colossus supercomputer cluster comprising 100,000 NVIDIA Hopper Tensor Core GPUs in Memphis, Tennessee, achieved this massive scale by using the NVIDIA Spectrum-X™ Ethernet networking platform, which is designed to deliver superior performance to multi-tenant, hyperscale AI factories using standards-based Ethernet, for its Remote Direct Memory Access (RDMA) network.