prism-ml · vision language

Ternary-Bonsai-27B-gguf

prism-ml/Ternary-Bonsai-27B-gguf

Ternary-Bonsai-27B-gguf at Q4_1 is exactly 1,946,393,568 bytes (1.81 GiB / 1.95 GB) — an effective 4.271 bits per weight, not the nominal 4.

From the file· summed from 1 file(s)
Parameters
3.6B
Architecture
qwen35
Context
—
native (config.json)
License
apache-2.0

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
Q4_11.81 GiB1,946,393,5684.271—Danny-Dasilva
Q2_06.67 GiB7,165,121,60015.722—Hikari07jp
Q2_07.68 GiB8,247,796,89618.098—Danny-Dasilva
Q2_07.68 GiB8,247,797,05618.098—dealignai

Compare with

same modality, comparable size
vs Qwen3.5-9Bvs gemma-4-12B-itvs Qwen3.5-4Bvs Qwen3.5-0.8Bvs gemma-4-E2B-itvs Qwythos-9B-v2All alternatives →Will it fit on my card? →

Will it run on your card?

full quant x context sweep
By VRAM:8GB12GB16GB24GB32GB48GB
Radeon RX 6500 XT 4GBGeForce RTX 3050 6GBGeForce RTX 5050 8GBGeForce RTX 3080 10GBGeForce RTX 2080 Ti 11GBGeForce RTX 5070 12GBGeForce RTX 5060 Ti 16GBApple M3 Pro 18GBGeForce RTX 3080 Ti 20GBGeForce RTX 5090 D V2 24GBGeForce RTX 5090 D 32GBApple M5 Max 36GBApple M5 Pro 48GBApple M5 Max 64GBApple M3 Ultra 96GBApple M5 Max 128GBApple M2 Ultra 192GBApple M3 Ultra 256GBApple M3 Ultra 512GB

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_1 at roughly 1.91 GiB. The real file is 1.81 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-27B-gguf need?
Q4_1 is exactly 1,946,393,568 bytes (1.81 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-27B-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.