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AI Research on NVIDIA DGX Spark
Pushing the Frontier of Local AI on a Petascale Desktop
This book is 100% completeLast updated on 2026-05-30
Serious AI research no longer needs a data center. This is the field log of training, fine-tuning, serving, and shipping real models on a single petascale desktop, with the code to reproduce every result. For engineers building AI on NVIDIA hardware who want depth, not hype.
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About the Book
AI Research on NVIDIA DGX Spark is a field guide to running real AI workloads on a petascale desktop. Its eight parts cover foundations, inference and retrieval, training, fine-tuning, agentic systems, observability, and deployment. Each one turns hands-on research into reproducible, code-backed lessons for engineers building AI on NVIDIA devices and GPUs.
Every chapter comes from real experiments on the DGX Spark, and pairs with the open-source Field Kit Python library, whose full API reference is included. If you want to push the frontier of local AI on the machine under your desk rather than in the cloud, this is the playbook.
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Manav Sehgal
Manav Sehgal is the founder of Orionfold, an AI studio that builds open AI software, custom models, and the books to run them locally and privately.
For twenty-five years he helped the biggest companies in the world put frontier technology in front of millions of people. He worked at Xerox PARC in the late 1990s, brought the cloud to businesses across India at AWS, led product for Alexa's conversational AI, worked on the foundation models that ship today as Amazon Nova, and most recently led the AWS team helping frontier AI labs like Anthropic and chipmakers like NVIDIA build on the cloud.
In 2026, after nearly nine years at Amazon, he left to do the opposite of big: put that same frontier in the hands of one person at a time. The belief behind every Orionfold book and tool is simple. The frontier of AI should not live only in giant clouds rented by giant companies. It should fit on your desk, run on your terms, and answer to you.
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