Velocity a proof of linear-scaling long context for existing LLMs, no retraining (github.com)

Velocity recently unveiled a groundbreaking local AI execution framework called Motify, which enables existing large language models (LLMs) to utilize long contexts without requiring retraining. This innovative system operates with .mfy artifacts and employs a new architecture known as Motify Transit Architecture (MTA). Unlike many AI applications, Velocity's focus is on the underlying execution stack rather than user-facing interfaces. The lightweight setup allows users to run models directly on their machines (Windows) using a simple executable, streamlining access to AI capabilities without cloud reliance. For the AI/ML community, Velocity's claims of linear-scaling long context represent a significant leap in efficiency. The MTA Adapt method allows models to maintain performance while reducing computational costs, achieving speeds up to 2.3 times faster than traditional methods with increased context length. Importantly, the absence of Python or server requirements makes deployment more accessible. Users can instantly benchmark performance against a reference using an integrated testing suite, reinforcing trust through verifiable results. This marks a pivotal step towards more efficient AI interactions in local environments, with potential implications for how AI models are integrated into various applications.