Bonsai Image
Ultra-Fast, Light-as-Air AI Generation
Bonsai Image launched on June 1, 2026 with 5 votes, #2,466 of 22,647 launches that month and more than 75% of that year's launches.
What it does
Generate high-fidelity AI images online with Bonsai Image. Powered by PrismML's optimized 1-bit & Ternary quantization of FLUX.2 Klein 4B. Highly responsive, cost-effective, and open.
Based on the FLUX.2 Klein 4B architecture, Bonsai Image keeps the powerful diffusion transformer intact while changing how weights are represented. The result is lightweight, fast, and highly capable generation. Traditional diffusion models require massive, power-hungry server clusters, driving up subscription costs and slowing down generation times. Bonsai Image solves this by shrinking the diffusion transformer from 7.75 GB to as little as 1.21 GB with minimal loss in visual fidelity. A lighter model means a faster, more accessible creative flow. Here is how Bonsai Image optimizes your digital art workflow at bonsaiimage.com. Whether you are using our online generator or running it…from bonsaiimage.com
Does a similar job
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1-Bit Bonsai, the First Commercially Viable 1-Bit LLMsMar 2026 · prismml.com · ▲430PrismML builds 1-bit and ternary Bonsai models that pack advanced reasoning, tool use, and multimodal AI into phones, laptops, and efficient data centers.

- RPRunning PrismML's Bonsai inside DRAM by breaking DDR4 timing rulesJul 2026 · ▲23
The excitement surrounding PrismML’s 1-bit/ternary Bonsai models has the industry closely watching how smartphone giants, particularly Apple, will implement LLMs on edge devices. Moving AI on-device is a brilliant and necessary strategy. It ensures absolute user privacy in alignment with EU regulations, fundamentally shifts the economics away from costly cloud inference, and paves the way for a significant hardware upgrade supercycle as users seek true AI-capable silicon. To create a smart on-device "Semantic Router," models need to reach the 27B+ parameter scale. Achieving this on a…


Bonsai 1.7B ternary model at 442T/s on M4 MaxMay 2026 · agents2agents.ai · ▲13We took a recently released Bonsai 1.7B ternary model from PrismML (https://github.com/PrismML-Eng/Bonsai-demo) and ran our agentic evolution search on it for 6 hours to optimize the Metal kernels. The search was fully autonomous. Measured against unmodified upstream llama.cpp at the same Bonsai/Q2_0 commit, same M4 Max: - tg128: 309.82 → 442.42 t/s (+42.0%) - pp512: 4250.32 → 4622.63 t/s (+8.8%)
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