Google's New Gemma 4 Models are Terrifyingly Small
We are past the era of needing massive cloud GPU clusters to run state-of-the-art AI. Google's new open weights pack massive performance in small sizes.
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Headline: Google鈥檚 new Gemma 4 models are terrifyingly small. 馃く
We are officially past the era of needing massive cloud GPU clusters to run state-of-the-art AI. Google just dropped the Gemma 4 family under a fully permissive Apache 2.0 license, and the size-to-performance ratio is wild.
The Benchmarks & Sizing:
- The dense 31B version is actively swinging above its weight class, providing competitive reasoning performance against massive models like Kimmy K2.5.
- The new E2B and E4B ("Effective Parameter") variants are so lightweight they can run locally on everything from an M4 Mac down to a mobile phone or a Raspberry Pi.
How is it this small?
- TurboQuant: A novel quantization breakthrough that compresses high-dimensional data into single sign bits, crushing the usual memory bandwidth bottlenecks.
- Per-Layer Embeddings: Instead of one massive embedding table, each model layer gets a custom token "cheat sheet." It injects information only when it is precisely needed.
Local, completely offline AI is no longer a toy鈥攊t is production-ready. You can pull this into Ollama and start running it right now.
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