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Description

Bonsai 27B stands as the latest multimodal flagship in the Bonsai lineup, marking the debut of a 27B-class model designed to operate on mobile devices. Built on the foundation of Qwen3.6 27B, it introduces an elevated level of capability for local devices, featuring advanced multi-step reasoning, structured tool interactions, vision tasks, and agentic loops for computer use that maintain coherence throughout multiple steps. The Bonsai 27B is available in two distinct variants. The Ternary Bonsai 27B employs ternary weights combined with FP16 group-wise scaling, achieving an effective weight of 1.71 bits and occupying a 5.9 GB footprint, suitable for high-performance laptop applications. In contrast, the 1-bit Bonsai 27B utilizes binary weights with identical group-wise scaling, resulting in an effective weight of 1.125 bits and a more compact 3.9 GB footprint, making it compatible with the memory constraints of devices like the iPhone 17 Pro. Both models operate seamlessly across the entire language network, including embeddings, attention mechanisms, MLPs, and the language model head, without resorting to higher-precision alternatives. They also feature a compact 4-bit vision tower, enabling on-device workflows to effectively interpret screenshots, documents, and camera inputs, enhancing user interaction and productivity. This innovative approach underscores Bonsai 27B's commitment to pushing the boundaries of mobile AI capabilities.

Description

Gemma 4 is an advanced AI model developed by Google as part of its Gemini architecture, designed to deliver strong performance while remaining accessible to developers. The model is optimized to run on a single GPU or TPU, allowing more organizations and researchers to experiment with powerful AI technology. Gemma 4 improves natural language understanding and generation, making it suitable for applications such as chatbots, text analysis, and automated content creation. Its architecture enables the model to process complex language patterns while maintaining efficient computational performance. Developers can integrate Gemma 4 into various AI projects that require intelligent text processing or conversational capabilities. The model is designed with scalability in mind, allowing it to support both research experiments and production systems. By offering high-performance AI in a more accessible format, Gemma 4 lowers the barrier for developing sophisticated AI solutions. Its flexibility makes it useful for industries ranging from technology and education to business automation. Researchers can also use the model to explore new AI techniques and improve language processing systems. Overall, Gemma 4 represents a step forward in making powerful AI models easier to deploy and use.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

No images available

Integrations

C++ No 
CSS No 
Clojure No 
Cloudflare AI Gateway No 
F# No 
FriendliAI No 
Gemini No 
Gemini Enterprise No 
HTML No 
Hugging Face No 
JavaScript No 
Kaggle No 
Kotlin No 
Locally AI No 
MedGemma No 
OpenTag No 
Parasail No 
PyTorch No 
Rust No 
Scala No 

Integrations

C++ Yes 
CSS Yes 
Clojure Yes 
Cloudflare AI Gateway Yes 
F# Yes 
FriendliAI Yes 
Gemini Yes 
Gemini Enterprise Yes 
HTML Yes 
Hugging Face Yes 
JavaScript Yes 
Kaggle Yes 
Kotlin Yes 
Locally AI Yes 
MedGemma Yes 
OpenTag Yes 
Parasail Yes 
PyTorch Yes 
Rust Yes 
Scala Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Deployment

Web-Based No 
On-Premises No 
iPhone App Yes 
iPad App Yes 
Android App No 
Windows No 
Mac Yes 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

PrismML

Founded

2026

Country

United States

Website

prismml.com/news/bonsai-27b

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

deepmind.google/models/gemma/

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