1-Bit Bonsai, the First Commercially Viable 1-Bit LLMs
PrismML builds 1-bit and ternary Bonsai models that pack advanced reasoning, tool use, and multimodal AI into phones, laptops, and efficient data centers.
1-Bit Bonsai, the First Commercially Viable 1-Bit LLMs launched on March 31, 2026 with 430 votes, #33 of 23,380 launches that month and more than 99% of that year's launches.
In plain words
1-Bit Bonsai offers commercially viable large language models that use 1-bit quantization, reducing computational requirements while maintaining functionality. The product targets developers and organizations seeking efficient AI solutions for resource-constrained environments. What distinguishes it is the focus on making extremely compressed LLMs practical for real-world deployment, balancing model size with performance in ways that 1-bit quantization previously struggled to achieve commercially.
written from the facts on this page · September 2026
From the sources
Bonsai Image 4B is a family of 1-bit and ternary image-generation models built for local devices. It brings compact diffusion inference to phones, laptops, and Macs, with open weights, Apache 2.0 code, and Bonsai Studio for on-device iPhone generation.
Available in 1-bit and Ternary variants, Bonsai 27B is the first 27B-class model capable of running on a phone. It brings multi-step reasoning, tool calling, agentic workflows, and multimodal understanding to local devices. Ternary Bonsai 27B is 5.9GB and optimized for laptops, while 1-bit Bonsai 27B is just 3.9GB - small enough to run on anfrom prismml.com
Does a similar job
all alternatives →- 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 – A Competitive Ternary Weight LLM2025 · github.com · ▲11Introducing Bonsai 0.5B, one of the first ternary-weight LLMs to rival full-precision models of similar size, such as Qwen 2.5 0.5B and MobileLLM 0.5B. Trained on just 3.8B tokens, using 1,000x less data than other models, Bonsai redefines what’s possible for ultra-efficient training in low-bit models. Next, we're building larger and more powerful ternary-weight models for the edge. Technical Report: https://github.com/deepgrove-ai/Bonsai/blob/main/paper/Bonsa... Model (Unpacked): https://huggingface.co/deepgrove/Bonsai Reach us:…
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%)
- WBWeb browser to help programmers think clearly2021 · bonsaibrowser.com · ▲1,070
I built a tiny LLM to demystify how language models workApr 2026 · github.com · ▲915Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.
Llama 3.2 Interpretability with Sparse Autoencoders2024 · github.com · ▲579I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3]. I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback. Cheers [1]…
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