Nader Khalil is NVIDIA’s Director of Developer Technology and the co-founder and former chief executive of Brev.dev, the developer-infrastructure company NVIDIA acquired in July 2024. He builds tools that make powerful computing and AI development environments easier for engineers—and increasingly autonomous agents—to use.
Khalil studied computer engineering at the University of California, Santa Barbara, and worked on cloud infrastructure at Workday before co-founding Paneau, a Y Combinator Winter 2020 startup. At Workday, integrated developer tooling streamlined the path from writing code to deployment; at Paneau, infrastructure and scaling diverted a small team’s attention from its customers.
After the pandemic forced Paneau to change direction, Khalil and collaborator Alec Fong founded Brev, initially operating from Khalil’s parents’ garage. The company began by simplifying deployment, hosting, and databases, then concentrated on developer-friendly GPU infrastructure as generative AI increased demand for specialized computing. Khalil co-authored a guide to one-click GPU deployment and model fine-tuning detailing how Brev simplifies provisioning, environment setup, and secure access.
Following the acquisition, Khalil continued building NVIDIA Brev, which combines GPU instances, preconfigured environments, and shareable deployments. He has also identified autonomous agents acquiring computing resources as an emerging audience for the platform.
His approach to practical AI
- Local AI for always-on agents. Persistent agents and reasoning systems consume computing resources even when users are not actively interacting with them. Khalil argues that local or controlled infrastructure can make those costs more predictable, protect sensitive information, and give organizations control over model-version changes.
- Agent harnesses and context. A model’s usefulness depends on its access to files, business systems, and other relevant inputs. Khalil points to coding assistants that work directly with a project’s filesystem as evidence that surrounding infrastructure can transform what capable models accomplish.
- Multi-model coordination. Combining specialized, local, and frontier models requires more than routing requests: applications must supply appropriate context, divide responsibilities, and preserve usable workflows. Khalil treats these orchestration problems as central to making sophisticated systems accessible.
- Open models and usable local infrastructure. Khalil advocates accessible open models and helped connect EXO Labs with NVIDIA specialists to improve local inference on DGX Spark. His conversation about local AI emphasizes practical optimization, approachable tooling, and the freedom to customize models.
Khalil’s writing about founder commitment argues that deliberate direction matters more than preserving endless options. He also maintains that AI-assisted creation does not eliminate professional expertise. Beyond software, he has imagined denser California-inspired architecture and proposed autonomous-vehicle lanes connecting California cities.
