Secure Local AI
Our work starts from a simple observation: much of the performance gap between local and hosted models is infrastructure, not capability. Improvement comes from unexpected places.
Our published Llama 3.1 8B and upcoming releases demonstrates measured benchmark improvement achieved on consumer hardware, using methods developed in-house.
The model is one output of a broader, patent-pending production system for local inference. A full paper and an open-source release of the system is forthcoming.
We look for gains in places the field tends to overlook — the systems around a model rather than the model's scale. The results so far have come from treating inference as a measured, verified process rather than a single pass.
We prefer to demonstrate rather than claim. Where we publish numbers, we publish the methodology that produced them.
The methods behind our published results can be adapted to specific domains, data constraints, and hardware. Inquiries are welcome.
Our methods are patent-pending and available for commercial licensing.