The decision-model lane is getting crowded: Microsoft built Microsoft-Decision-1 on a Qwen3.5-9B base, Amazon open-sourced a 2B decision model with training scripts, Perplexity released the 27B pplx-decider, and Cloudflare shipped Clef. All of these share the same implicit assumption — entering the lane means shipping a new model. IBM took a different route: don't train a new model; let one model switch between chat, decisions, and speech.
One endpoint, three capabilities
IBM released Granite Switch 4.1 on Hugging Face in 8B and 30B sizes under Apache 2.0. It is not another standalone decision model: on top of the Granite base model it mounts a Decision adapter and what IBM calls the fastest ASR model on the OpenASR leaderboard (granite-speech-5.0-470m-turboctc), serving natural-language responses, general and specialized decisions, and speech-to-text from a single hosted endpoint. IBM stresses that the composition takes roughly the same VRAM as the bare base model — more capabilities, no extra GPUs.
The key mechanism: KV cache reuse
The engineering backbone is cache-efficient Activated LoRAs (aLoRA): adapters do not each keep their own inference state; the KV cache built during base-model generation is reused by subsequent adapters. Two supporting moves: the team contributed low-rank adapter inference kernels upstream to vLLM, and the ASR encoder is co-located with the text model. In the official HF Space demo, the 8B version transcribes audio, generates summaries, screens for harmful content, and tests 15 user-defined flags in real time on a single endpoint — a pipeline that would normally string three services together.
On top of this, IBM's earlier Granite Libraries is itself a library of specialized decision adapters; IBM says these specialists can outperform generalist decision models like Jev on specialized classification tasks. And because they share the base KV cache, adding specialists next to a generalist costs almost nothing.
Self-reported numbers: apply a discount
The two catchiest comparison numbers both come from official demos. First, a custom 3B Granite Switch playing Doom made 60-100 decisions per second — IBM notes Jev's reported figure is 10 per second, cited from its official blog — and it can narrate and answer questions while playing. Second, the 30B version (frozen base plus 4 specialized aLoRAs) raced Jev-27B (frozen Qwen base plus 1 generic decision LoRA) through a real-world five-step RAG workflow and finished faster. Demos are not independent benchmarks: the direction is worth noting, the numbers less so.
So what
In this decision-model wave, the "model" itself may not be the moat: bases are public, fine-tuning is table stakes, and Microsoft has already said it will swap Qwen for its own and OpenAI's models. IBM is betting on a different layer — the engineering of composition and switching: one endpoint, one allocation of VRAM, a pluggable adapter ecosystem, with an Adapter SDK planned for third parties. For VRAM-sensitive enterprise and edge deployments, "three-in-one without extra VRAM" is a bill that adds up. Until third-party replication arrives, treat it as a route worth tracking, not a victory already won.
Reference: IBM Granite team announcement — https://huggingface.co/blog/ibm-granite/granite-switch-4-1