What GPU do I need to run ibm-granite/granite-4.1-8b-fp8?

8.8B parameters, published in F8_E4M3. View on Hugging Face

8.8B
Parameters
F8_E4M3
Native precision
GraniteForCausalLM
Architecture
text-generation
Pipeline

granite-4.1-8b-fp8 is published by ibm-granite on Hugging Face, with 22,322 downloads and 14 likes to date. It's a GraniteForCausalLM model built for text-generation, published natively in F8_E4M3.

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)
FP8 (native)
8.2 GB
9.8 GB
RTX 4070 (simplepod)
1
$0.080/hr
INT4 (quantized)
4.1 GB
4.9 GB
RTX 3070 (simplepod)
1
$0.050/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 granite-4.1-8b-fp8 at its published (F8_E4M3) precision: 1× RTX 4070 on simplepod, at $0.080/hr per GPU ($0.080/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 ibm-granite 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.