In an industry where model size is often seen as a proxy for intelligence, IBM is charting a different course — one that values efficiency over enormity, and accessibility over abstraction.The 114-year-old tech giant’s four new Granite 4.0 Nano models, released today, range from just 350 million to 1.5 billion parameters, a fraction of the size of their server-bound cousins from the likes of OpenAI, Anthropic, and Google. These models are designed to be highly accessible: the 350M variants can run comfortably on a modern laptop CPU with 8–16GB of RAM, while the 1.5B models typically require a GPU with at least 6–8GB of VRAM for smooth performance — or sufficient system RAM and swap for CPU-only inference. This makes them well-suited for developers building applications on consumer hardware or at the edge, without relying on cloud compute.In fact, the smallest ones can even run locally on your own web browser, as Joshua Lochner aka Xenova, creator of Transformer.js and a machine learning engineer at Hugging Face, wrote on the social network X.All the Granite 4.0 Nano models are released under the Apache 2.0 license — perfect for use by researchers and enterprise or indie developers, even for commercial usage. They are natively compatible with llama.cpp, vLLM, and MLX and are certified under ISO 42001 for responsible AI development — a standard IBM helped pioneer.But in this case, small doesn’t mean less capable — it might just mean smarter design.These compact models are built not for data centers, but for edge devices, laptops, and local inference, where compute is scarce and latency matters. And despite their small size, the Nano models are showing benchmark results that rival or even exceed the performance of large …
