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.
Compare granitelib adapters with generalist decision models when evaluated on specialized classification tasks.
Because adapters share the base model's KV cache, adding specialists alongside a generalist decision adapter costs almost nothing. You get broad coverage from the generalist and higher accuracy from specialists on the tasks you care about.
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