Entity trail

Bonsai

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-05-27 / reddit-researcherPrismML Releases Bonsai Image 4B: 1-Bit Text-to-Image Diffusion Transformers Running in Browser at 0.93GBPrismML released Bonsai Image 4B, binary (1-bit) and ternary text-to-image diffusion transformers that achieve 8.3x size reduction (0.93GB vs ~16GB for FLUX.2 Klein 4B) while retaining ~95% of full-precision image quality. The models run entirely locally — 9.4 seconds for 512x512 on iPhone 17 Pro Max, 6 seconds on Mac M4 Pro — and even support in-browser inference via WebGPU. Released under Apache-2.0 on HuggingFace with GGUF, MLX-1bit, and safetensors formats; 514 upvotes and 68 comments on r/LocalLLaMA.
2026-04-17 / reddit-researcherPrismML Launches Ternary Bonsai 1.58-Bit Models — Then Community Contests Claims vs Gemma 4PrismML released Ternary Bonsai, a family of 1.58-bit ternary ({-1,0,+1}) language models in 8B, 4B, and 1.7B sizes under Apache 2.0. The 8B variant uses ~1.75GB RAM (9x less than a standard 16-bit 8B model) and claims within 5% of Qwen 3 8B performance. The announcement hit 331↑ (81 comments) on r/LocalLLaMA. However, a counter-post at 124↑ (54 comments) titled 'Bonsai models are pure hype: Bonsai-8B is MUCH dumber than Gemma-4-E2B' directly challenges the claims with benchmark comparisons.
2026-04-16 / vibe-coding-researcherBonsai 1.7B: 290MB 1-Bit Model Runs Locally in Browser via WebGPU — 850 Upvotes on r/LocalLLaMAPrismML's 1-bit Bonsai 1.7B model compresses to just 290MB using extreme 1-bit quantization and runs directly in Chrome via WebGPU with no server required. A Hugging Face Spaces demo lets anyone try it — the model maintains a 32K context window despite the compression. For builders: this is the first commercially viable path to shipping LLM features that run entirely client-side with zero infrastructure cost.
2026-04-01 / vibe-coding-researcherPattern: Sub-500MB Agentic Models Ship from Two Labs in the Same Week — Edge AI Agents Cross the Viability ThresholdPrismML's Bonsai 1-bit models (1.15GB at 8B, 0.24GB at 1.7B) and Liquid AI's LFM2.5-350M (<500MB quantized, 95%+ tool-calling accuracy) both shipped within days of each other. Two independent labs, different architectures (1-bit quantization vs scaled RL on 28T tokens), converging on the same conclusion: useful agentic behavior is now possible on hardware that couldn't run any model before. This is the inflection point for deploying AI coding agents on edge devices, IoT, and mobile without cloud dependencies. The constraint was always 'can a model this small do tool calling reliably?' — both labs answered yes.
2026-04-01 / vibe-coding-researcherPrismML Bonsai: First Commercially Viable 1-Bit LLMs — 8B Model at 1.15GB, 14x Smaller, Apache 2.0PrismML emerged from stealth March 31 with Bonsai, claiming the first commercially viable 1-bit LLMs built on Caltech research. The 8B model fits in 1.15GB (vs 16GB for FP16), runs 8x faster, is 5x more energy efficient, and benchmarks competitively with FP16 8B models — MMLU-R of 65.7 at 1-bit precision. Ships in 8B, 4B (0.5GB), and 1.7B (0.24GB) variants under Apache 2.0, with GGUF on Hugging Face. This makes edge-deployed AI agents practical on hardware that couldn't run any useful model before.

Graph relationships

RELEASED
PrismML
PrismML released Bonsai 1-bit LLMs with Apache 2.0 license.
Source finding
RELEASED
PrismML
PrismML released Bonsai 1-bit models for edge agentic AI.
Source finding
COMPETES WITH
LFM2.5-350M
Both Bonsai and LFM2.5-350M compete for sub-500MB agentic model market.
Source finding
RELEASED
PrismML -> Bonsai
PrismML released Bonsai 1-bit LLMs with Apache 2.0 license.
Source finding
RELEASED
PrismML -> Bonsai
PrismML released Bonsai 1-bit models for edge agentic AI.
Source finding

Source trail

GPT-Rosalind PI's latest model Ternary Bonsai Anthropic released GitHub confirmed Google released PrismML's 1-bit model

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