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# “We love the world where we can use both”: How Nvidia thinks about local and frontier models

**[The New Stack](https://daily.dev/sources/newstack)** · 4 min read · 0 upvotes · 0 comments

## Summary

Nvidia's Joey Conway outlines a 'system of models' approach where small local models and large frontier models split workloads based on cost, latency, and complexity. A router sits between them, directing simple tasks to fast local models and harder tasks to cloud-based frontier models. Conway emphasizes that enterprises value control over their data as much as cost savings, and that fine-tuned open models can serve as proprietary knowledge stores. Nvidia's hardware (DGX Spark, DGX Station), inference software (Dynamo), and agent stack (NemoClaw/OpenClaw) support this hybrid architecture. A collaboration with LangChain showed Nvidia's 550B-parameter Nemotron Ultra matching top closed models at up to 10x lower cost with no retraining required.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://thenewstack.io/nvidia-local-frontier-models>

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Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#nvidia](https://daily.dev/tags/nvidia), [#local-ai](https://daily.dev/tags/local-ai), [#ai-inference](https://daily.dev/tags/ai-inference)

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