Nope :) the core model isn’t an LLM. It’s a custom architecture built from the ground up. We natively accept multimodal inputs such as source code, submission scripts and hardware topologies. The LLMs in the post are the baselines we beat.

This is also why fine-tuning matters for us. We train a cluster-specific model that gets better as more jobs run on your cluster, because the same code behaves differently on different topology. An LLM reasons about code/script in a vacuum with no native sense of how your nodes actually perform