What GPU do I need to run KridgeDookie/Qwen3.8-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS?

27.4B parameters, published in BF16. View on Hugging Face

27.4B
Parameters
BF16
Native precision
Qwen3_5ForConditionalGeneration
Architecture
image-text-to-text
Pipeline

Qwen3.8-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS is published by KridgeDookie on Hugging Face, with 43,532 downloads and 10 likes to date. It's a Qwen3_5ForConditionalGeneration model built for image-text-to-text, published natively in BF16.

VRAM required & cheapest live GPU fit

Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.

Precision
Weight size
Required VRAM
Cheapest live fit
GPUs needed
Est. $/hr (full fit)
BF16
51.0 GB
61.1 GB
A100 (runpod)
1
$1.19/hr
cheaper alt.
RTX 3070 (simplepod)
8
$0.400/hr
FP8 (quantized)
25.5 GB
30.6 GB
RTX 4080 Super (simplepod)
1
$0.380/hr
cheaper alt.
RTX 5060 Ti (simplepod)
2
$0.200/hr
INT4 (quantized)
12.7 GB
15.3 GB
RTX 5060 Ti (simplepod)
1
$0.100/hr

A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.

INT4 caveat: requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.

Cheapest way to run Qwen3.8-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS at its published (BF16) precision: 1× A100 on runpod, at $1.19/hr per GPU ($1.19/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.

Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.

More KridgeDookie models

Ready when you are

Your next GPU already
has your environment on it.

Sign up in 60 seconds. Pay for the GPU minutes you actually use.

© 2026 Aquanode. All rights reserved.

All trademarks, logos and brand names are the property of their respective owners.