IBM’s Granite 4.0 Nano: Tiny LLMs That Run in Your Browser

IBM has just released the Granite 4.0 Nano family, a set of four open-source language models that weigh in at only 350 million to 1.5 billion parameters. The tiny footprint means the 350M variants can run comfortably on a modern laptop CPU with 8–16GB of RAM, while the 1.5B models need only a modest GPU or a CPU with ample RAM and swap. In fact, the smallest models can even be loaded directly into a web browser, allowing developers to experiment with full-scale language capabilities on the edge without any cloud dependency. By making the weights available under an Apache 2.0 license and certifying the models with ISO 42001, IBM is positioning Granite as a trustworthy, privacy-friendly alternative to the monolithic APIs that dominate the space.

The Nano line includes both hybrid-state-space (SSM) and pure-transformer variants. The Granite-4.0-H-1B and H-350M use a hybrid SSM architecture that blends attention with memory-efficient state-space layers, delivering low-latency inference on constrained hardware. The standard transformer models, Granite-4.0-1B (≈2B parameters) and 350M, are fully compatible with popular runtimes such as llama.cpp, vLLM, and MLX, making them accessible to the broader open-source community. Benchmark results on IFEval, BFCLv3, and safety tests show the Nano models outperform or match peers in the sub-2B class, with the H-1B scoring 78.5 on instruction following and the 1B model achieving a 68.3% average across knowledge, math, code, and safety domains.

Beyond the numbers, IBM’s launch comes with a clear roadmap: a larger Granite 4.0 model is already in training, reasoning-focused variants are slated for release, and fine-tuning recipes will soon be published. The team has already engaged the community through Reddit AMAs and open-source forums, demonstrating a commitment to transparency and collaboration. By offering models that run locally, maintain privacy, and are open under a permissive license, IBM is redefining what it means to build powerful AI for developers who need speed, flexibility, and auditability.