Artificial Intelligence
PrismML
PrismML is an AI-native company in Artificial Intelligence, based in United States. Shown below: 12 models.
US
- Employees
- 6
- Valuation / employee
- $25M
- Listed models
- 12
Figures and classifications reflect imported catalog records, not live company reporting.
Models
12
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Bonsai 27B AWQ 4bit Gen 3 Multimodal
A 4-bit AWQ quantized release of Prism ML's Bonsai 27B, a 27 billion parameter multimodal model that accepts image and text input. It supports multi-step reasoning, tool calling, and agentic workflows, and is built for…
- Chatting
- Images
- Image analysis New
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Ternary Bonsai 27B AWQ 4bit Gen 3 Multimodal
Qwen Ternary Bonsai 27B AWQ 4bit is a 4 bit AWQ quantized build of PrismML's Ternary Bonsai 27B, a multimodal model based on Qwen3.6 27B. It accepts text and images, supports a 262K token context window, and is designed…
- Chatting
- Coding help
- Image analysis New
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Ternary Bonsai 27B Unpacked Gen 3 Multimodal
Qwen Ternary Bonsai 27B is a 27B parameter multimodal language model from PrismML, built on a hybrid linear and full attention (Qwen3.5 based) architecture with a 262,144 token context window. This repository provides t…
- Chatting
- Images
- Agents New
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Bonsai 27B Gen 3 Multimodal
Qwen Bonsai 27B is a 27.8B-parameter multimodal language model built on Qwen3.6 27B, quantized end-to-end to 1-bit or 1.58-bit ternary weights across embeddings, attention, MLPs, and the LM head. It accepts text and ima…
- Chatting
- Coding
- Math New
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Ternary Bonsai 27B Gen 3 Multimodal
Qwen A 27B parameter language model compressed to ternary (1.71 bit) weights, retaining 95% of full precision intelligence while shrinking deployed size to about 7.2 GB. Runs full 27B class reasoning, math, and coding o…
- Chatting
- Coding
- Math New
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Ternary Bonsai 27B GGUF Gen 3 Multimodal
Qwen Ternary Bonsai 27B GGUF is a 27B parameter reasoning language model compressed to true 2 bit ternary weights (1.71 bits per weight), derived from a Qwen3.6-27B hybrid attention backbone. It runs in a roughly 7.2GB…
- Chatting
- Coding
- Math New
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Bonsai 27B mlx 1bit Gen 3 Multimodal
Qwen 1-bit quantized version of the 27B-parameter Qwen3.6-27B language model, using binary g128 weight representation (1.125 bits per weight) for a ~3.9GB deployed footprint, about 14.2x smaller than FP16. Supports a 26…
- Chatting
- Coding
- Math New
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Ternary Bonsai Image 4B Gen 4 Image
PrismML’s open-weight compressed image-generation model that trades slightly larger size for stronger visual quality and prompt fidelity.
- Image generation
- Photorealistic images
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1-bit Bonsai Image 4B Gen 4 Image
PrismML’s open-weight compressed image-generation model built for local diffusion inference on laptops, phones, and other memory-limited devices.
- Image generation
- Photorealistic images
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1 Bit Bonsai 1.7B Gen 7 Text
1-bit Bonsai 1.7B is PrismML’s smallest Bonsai model, built for extremely fast on-device inference. With a memory footprint of just 0.24GB, PrismML says it can reach 130 tokens per second on an iPhone 17 Pro Max, making…
- AI inference
- Ai deployment management
- AI cost management
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1 Bit Bonsai 4B Gen 7 Text
1-bit Bonsai 4B is PrismML’s mid-size ultra-efficient model designed for fast local inference. It uses just 0.57GB of memory and is positioned as a strong balance of speed, accuracy, and energy efficiency, with PrismML…
- AI inference
- Ai deployment management
- AI cost management
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1 Bit Bonsai 8B Gen 7 Text
1-bit Bonsai 8B is PrismML’s flagship ultra-efficient language model, built with 1-bit weights for edge and real-time use. It needs only 1.15GB of memory and is aimed at robotics, real-time agents, and on-device computi…
- AI inference
- Ai deployment management
- AI cost management
Source rankings
Imported positions; not an independent quality score.
- Overall Rank#1422
- AI Native Rank#677
- Rank#711