Running PrismML's Bonsai inside DRAM by breaking DDR4 timing rules
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…
Running PrismML's Bonsai inside DRAM by breaking DDR4 timing rules launched on July 23, 2026 with 23 votes, #817 of 24,623 launches that month and more than 90% of that year's launches.
In plain words
PrismML's Bonsai is a technique for running large language models directly on smartphones by using extreme quantization and optimizing memory access patterns in DRAM. It enables 27+ billion parameter models on mobile devices by breaking standard DDR4 timing rules to overcome memory bandwidth limitations. The system is designed for smartphone manufacturers seeking to implement on-device AI while preserving user privacy and reducing cloud inference costs.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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…
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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.
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%)
Cactus – Ollama for Smartphones2025 · github.com · ▲231Hey HN, Henry and Roman here - we've been building a cross-platform framework for deploying LLMs, VLMs, Embedding Models and TTS models locally on smartphones. Ollama enables deploying LLMs models locally on laptops and edge severs, Cactus enables deploying on phones. Deploying directly on phones facilitates building AI apps and agents capable of phone use without breaking privacy, supports real-time inference with no latency, we have seen personalised RAG pipelines for users and more. Apple and Google actively went into local AI models recently with the launch of Apple Foundation Frameworks…
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Source: Show HN post ↗ · launch text quoted in part
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