Performance per dollar
- Benchmarking Nucore's modified compute against NVIDIA and cloud instances on agentic workloads.
Research at Nucore
We publish the work behind our products: how we squeeze more agentic throughput out of every watt, how we keep powerful models running privately on-premise, how we stop sensitive data from leaving the building, and how we turn raw data into training fuel.
Private & agentic infrastructure
- Running powerful LLMs and multi-agent systems entirely on owned, cross-organization infrastructure.
Data security & data engines
- Preventing data leakage at the proxy layer, and automated annotation that feeds model growth.
Performance-per-Dollar of Co-Designed Compute for Agentic AI Workloads
A measurement study of Spartan units against NVIDIA reference cards and major cloud GPU instances on long-horizon, multi-agent benchmarks.
A. Rao, M. Chen, L. Samineni · March 2026
Read →Kronos: Running Powerful LLMs on Private, Cross-Organization Infrastructure
A reference architecture for serving and fine-tuning frontier-scale models on owned hardware shared across teams, with no data leaving the perimeter.
J. Okafor, P. Nair · February 2026
Read →Cloak: An Agentic Proxy Layer for Preventing Sensitive Data Leakage Across Enterprise AI
A real-time detection-and-redaction system that sits as a trusted third party between employees and every AI tool an organization uses.
S. Whitfield, D. Park, A. Rao · April 2026
Read →Helios: Automated Annotation Pipelines for High-Quality LLM Training Data
How Nucore's data engine ingests raw, unstructured data and produces human-validated, labeled datasets that drive measurable model improvements.
M. Chen, R. Delgado · May 2026
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