Specifically, Granite-Switch-4.1-8B and -30B models are configured to include:

A new Decision aLoRA for general-purpose decision-making (e.g. v1/systemone), A specialized Guardian aLoRA that, on average, outperforms Jev 1.13, Microsoft-Decision-1, GPT-Luna-Decisions, and Clef-Flash on safety-related decision tasks, and Granite-Speech-5.0-Turbo-CTC, the fastest ASR model on the OpenASR Leaderboard, embedded for use in transcription tasks.

All three of these technologies (along with the underlying base model) are composed into one model, taking up approximately the same amount of VRAM on a GPU as the base model would by itself. That means more capabilities with fewer GPUs.

And because the Switch adapters were built as cache-efficient Activated LoRAs, the KV cache created from a base model generation can be reused for any subsequent adapter-based decisions. Combined with our team's recent vLLM contributions to speed up low rank adapter inferencing, as well as a co-located ASR encoder, Granite Switch 4.1 models can switch between a chat request and a decision request and a spoken request, with blazing fast speeds.

Check out our Speech-to-Decisions HF space where granite-switch-4.1-8b transcribes audio, generates summaries, screens for harmful content, and tests 15 other user-defined flags—all running on a single endpoint in real time.

Decision models aren’t new. Our previous release of Granite Libraries features a repository of specialized “decision model” adapters and other specialized task adapters that can be composed into custom Granite Switch models. And while Jev and other closed decision models focus on generality, we’ve found that many of our specialized granitelib adapters can outperform generalists on specialized classification tasks.