The Lab
The hardware isn't the story — what it enables is. Dedicated infrastructure for local AI inference, distributed compute, and self-hosted services that don't phone home.
Capabilities
Local Inference
Multiple concurrent LLMs running on dedicated GPU hardware. Inference stays on-prem, never routed through a third-party API. Zero telemetry by architecture.
Distributed Compute
Multi-machine workload distribution for batch inference, model testing, and reproducible benchmarking. No cloud instances, no per-token billing.
Self-Hosted Services
Containerized infrastructure — everything from CI runners to AI agents runs under the same roof. No vendor lock-in, no surprise terms-of-service changes.
Offline-First Operations
Designed to keep working when connectivity drops. Local models, local storage, local decision-making. Resilience as architecture, not afterthought.
Software Stack
Why Local?
Every prompt to a closed cloud service becomes training data. Every file uploaded to a remote API is stored, analyzed, and potentially reproduced. For client work, proprietary code, and regulated data, that's unacceptable.
Local models run at production speeds on modern hardware. They're private, cost nothing per token, and work offline. You don't need a monthly subscription to get capable AI — you need the right architecture and the discipline to keep it local.
When cloud is used, it's chosen consciously — not by default. That's the difference.