IBM has introduced the Granite 4.0 Nano family, a set of four ultra‑compact language models that range from 350 million to 1.5 billion parameters. Because the models are so small, they can run on a modern laptop CPU, a mid‑range GPU, or even directly in a web browser with no cloud dependency. All versions are released under the permissive Apache 2.0 license, making them immediately usable for research, indie projects, or commercial products.
The Nano line is split into hybrid state‑space (SSM) models—Granite‑4.0‑H‑350M and H‑1B—and classic transformer variants—Granite‑4.0‑350M and 1B (the latter is closer to 2 B in practice). The hybrid SSM design blends transformer attention with Mamba‑2 memory‑efficient layers, yielding low latency and high context windows on edge hardware. In IFEval and BFCLv3 benchmarks the H‑1B scored 78.5 and the 1B model 54.8, topping competitors such as Qwen‑3 1.7B and Mistral’s sub‑2 B offerings while also hitting safety scores above 90 % on SALAD and AttaQ.
Beyond performance, IBM is positioning Granite as an enterprise‑first, responsible AI platform. The models are ISO 42001 certified, cryptographically signed, and supported by popular inference engines like llama.cpp, vLLM, and MLX. Community engagement on Reddit’s r/LocalLLaMA and the release of fine‑tuning recipes underline a roadmap that includes larger models, reasoning‑focused variants, and deeper tooling. In short, the Nano release signals a strategic pivot in AI development: prioritizing efficient, locally deployable models over ever‑growing parameter counts.
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