bonsai

Prism-ML announces that their ternary Bonsai models can now be fine-tuned, with example code and a recommendation to use a high learning rate.

A detailed guide on running the 27B Bonsai model on hardware with only 8GB VRAM using a 1-bit quantized version and the PrismML fork of llama.cpp, including exact server commands and configuration.

A 1-bit LLM (Bonsai) is now runnable in the browser via WebGPU, enabling efficient on-device inference.

PrismML announces Bonsai 27B, a binary-quantized version of Qwen3.6 27B that runs on a phone using only 1.125 bits per weight, claiming 89.5% intelligence retention. The model is being independently tested by @sudoingX to verify performance.

A new ternary quantized version of Qwen3.6 27B, called Bonsai 27B, allows running the model on 12GB GPUs with 10x less memory and 95% of original performance, making it accessible for local deployment.

Discussion on the feasibility of 1-bit models like Bonsai 8b and 27b, which achieve small file sizes while remaining functional, questioning their primary use cases and future.

Prism-ML published benchmarks for their Bonsai-27B model.

Ternary Bonsai 27B, a large language model, is demonstrated running locally on an NVIDIA RTX 5090 GPU, requiring under 6GB of memory and enabling end-to-end agentic workflows on consumer hardware.

PrismML announces Bonsai 27B, a multimodal model based on Qwen3.6 27B that can run on a phone, enabling local multi-step reasoning, tool use, and long-context workflows.

PrismML announces Bonsai 27B, a 1-bit and ternary quantized version of Qwen3.6 27B that runs on phones and laptops, retaining 90-95% of baseline performance with a 3.9GB footprint, enabling agentic and multimodal on-device AI.

Prism ML released Ternary-Bonsai-27B, a ternary-quantized version of Qwen3.6-27B that retains 95% of FP16 intelligence at a ~7.2 GB footprint, enabling full 27B-class reasoning on laptops and single GPUs with speeds up to 26 tok/s on Apple M5 Pro.

Prism ML releases Ternary-Bonsai-27B-mlx-2bit, a ternary-quantized 27B-parameter language model that achieves ~95% of FP16 performance while fitting in ~7.2 GB, enabling full reasoning on laptops.

Jane Street engineers introduce strace-ui, an interactive terminal UI for strace that simplifies syscall debugging with filtering, PID tracking, and man page integration, and highlight the TUI renaissance enabled by their Bonsai framework.

PrismML releases Bonsai 27B, a vision-language model with agentic tool calling and long context, along with 1-bit and ternary variants. The demo repository allows running these models locally on various hardware.