Google Gemma 4 vs Ternary Bonsai

Google Gemma 4 is the most capable open model family Google has released, and the first to unify text, vision, and audio in a single architecture — all under the Apache 2.0 license. Available in four sizes (E2B, E4B, 26B MoE, 31B Dense), the lineup runs everywhere from smartphones to high-end GPUs and covers 140+ languages with context windows up to 256K. The headline stat: the 31B Dense model benchmarks above models nearly 20x its size in certain evals, making it the sharpest intelligence-per-parameter model in the open-source ecosystem as of its April 2026 release. The multimodal architecture processes documents with OCR, analyzes charts, transcribes speech, and understands video frames from a single model — no pipeline stitching required. For developers and researchers, the Apache 2.0 licensing is the real unlock. Gemma 4 is fully OSI-approved and commercially usable without restriction, building on a community of 400M+ downloads from prior Gemma versions and 100,000+ variants in the wild.

PrismML's Ternary Bonsai is a family of ultra-compressed language models using 1.58-bit weights — meaning every parameter is stored as -1, 0, or +1, with no higher-precision layers anywhere in the architecture. The line-up covers 8B, 4B, and 1.7B parameter models. The flagship 8B model fits in 1.75 GB of RAM, a 9x reduction versus a 16-bit baseline. Unlike earlier 1-bit experiments that felt like a party trick with serious capability regressions, Ternary Bonsai 8B outperforms PrismML's own prior 1-bit Bonsai 8B by 5 points on average across standard benchmarks. The team also ships WebGPU inference, so the 1.7B model runs entirely in a browser tab. This is the first time a production-quality chat model has run with no server at all. The real-world use case is edge and offline deployment: medical devices, air-gapped government systems, consumer apps that need to work without a signal. At 1.75 GB, the 8B model fits on the GPU RAM of a six-year-old gaming laptop. PrismML is positioning this as the foundation for truly offline AI — a credible claim if the capability benchmarks hold up under real-world testing.