What are the hardware requirements for Ternary Bonsai 2 27B Vllm?

Ternary Bonsai 2 27B Vllm requires approximately 10.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Ternary Bonsai 2 27B Vllm and how large is it?

Ternary Bonsai 2 27B Vllm is developed by fraserprice, a model with 27b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Ternary Bonsai 2 27B Vllm?

Ternary Bonsai 2 27B Vllm is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Ternary Bonsai 2 27B Vllm by fraserprice — VRAM 10.3GB

Name: Ternary Bonsai 2 27B Vllm
Author: fraserprice

Ternary Bonsai 2 27B Vllm is an open-source language model by fraserprice. Features: 27b LLM, VRAM: 10.3GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.36.

2-bit 2bit Base model:prism-ml/ternary-bo... Base model:quantized:prism-ml/... Bonsai Conversational Cuda Prism ternary Prismml Quantized Qwen3 5 Region:us Safetensors Sharded Tensorflow Ternary Vllm

Model Card on HF 🤗: https://huggingface.co/fraserprice/Ternary-Bonsai-2-27B-vllm

Ternary Bonsai 2 27B Vllm Parameters and Internals

LLM Name	Ternary Bonsai 2 27B Vllm
Repository 🤗	https://huggingface.co/fraserprice/Ternary-Bonsai-2-27B-vllm
Base Model(s)	prism-ml/Ternary-Bonsai-2-27B-gguf Ternary Bonsai 2 27B Mlx 2bit prism-ml/Ternary-Bonsai-2-27B-gguf prism-ml/Ternary-Bonsai-2-27B-mlx-2bit
Model Size	27b
Required VRAM	10.3 GB
Updated	2026-09-21
Maintainer	fraserprice
Model Type	qwen3_5
Model Files	3.3 GB: 1-of-3 3.3 GB: 2-of-3 3.7 GB: 3-of-3
Quantization Type	2bit
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.8.0.dev0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Ternary Bonsai 2 27B Vllm capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Ternary Bonsai 2 27B Vllm

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.6 27B OptiQ 4bit	256K / 18.7 GB	13065	59
Bonsai 27B Mlx 1bit	0K / 5.1 GB	809309	205
Ternary Bonsai 27B Mlx 2bit	0K / 8.5 GB	809024	168
Qwen3.8 27B MLX 5bit	0K / 19.5 GB	4675044	2
...onsai 2 27B CRACK Ternary JANG	0K / 8.1 GB	2689	13
Qwen3.6 27B MLX 8bit	0K / 29.4 GB	879145	14
Qwen3.6 27B MLX 6bit	0K / 22.7 GB	812977	2
Qwen3.8 27B 8bit	0K / 29.4 GB	13892	33
...onsai 2 27B CRACK 1.75bit JANG	0K / 6.4 GB	1457	3
Qwen3.6 27B MLX 4bit	0K / 16 GB	846861	16

Note: green Score (e.g. "73.2") means that the model is better than fraserprice/Ternary-Bonsai-2-27B-vllm.