How much VRAM does Ternary-Bonsai-8B-gguf need?

TQ2_0 is exactly 2,118,214,048 bytes (1.97 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

prism-ml · text

Ternary-Bonsai-8B-gguf

prism-ml/Ternary-Bonsai-8B-gguf

Ternary-Bonsai-8B-gguf at TQ2_0 is exactly 2,118,214,048 bytes (1.97 GiB / 2.12 GB) — an effective 2.069 bits per weight, not the nominal 2.

From the file· summed from 1 file(s)

Parameters

8.2B

Architecture

qwen3

Context

—

native (config.json)

License

—

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
TQ2_0	1.97 GiB	2,118,214,048	2.069	—	Minarut
TQ2_0	2.47 GiB	2,656,942,336	2.596	—	ewchampion

Compare with

same modality, comparable size

vs gemma-4-E4B-it vs Qwen3-4B vs Qwen3-8B vs HyperCLOVAX-SEED-Text-Instruct-1.5B vs Llama-3.2-1B-Instruct vs Qwen3-VL-8B-Instruct-abliterated-v1 All alternatives →Will it fit on my card? →

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts TQ2_0 at roughly 4.29 GiB. The real file is 1.97 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

Architecture unavailable — this repository is gated and no ungated mirror was found. Exact file sizes above are still authoritative; only the KV math needs the config.

Questions people ask

How much VRAM does Ternary-Bonsai-8B-gguf need?: TQ2_0 is exactly 2,118,214,048 bytes (1.97 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of Ternary-Bonsai-8B-gguf should I use?: Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.