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Head to head

Gemini 1.0 Ultra vs Llama Nemotron Ultra 253B

Google DeepMind
Gemini 1.0 Ultra
December 2023
vs
5×10²⁵Training compute (FLOP)3.9×10²⁵
$31MTraining cost--

Gemini 1.0 Ultra (Google DeepMind) and Llama Nemotron Ultra 253B (NVIDIA) are both frontier AI models. Gemini 1.0 Ultra was published in December 2023 and Llama Nemotron Ultra 253B in March 2025.

Gemini 1.0 Ultra was trained on 5×10²⁵ FLOP, about 1.3x the compute of Llama Nemotron Ultra 253B at 3.9×10²⁵ FLOP. Training compute is the closest available proxy for how much was invested in a model, though it says nothing on its own about how well that compute was spent.

These two models share no benchmark on which both have been scored, so no direct performance comparison is possible here. The specification table below is a comparison of inputs, not of results.

Specifications
Google DeepMind
Organization
NVIDIA
Dec 6, 2023
Published
Mar 18, 2025
5×10²⁵ FLOP
Training compute1.3x
3.9×10²⁵ FLOP
--
Parameters
253B
--
Dataset size
603B
Google TPU v4
Training hardware
--
57,000
Chips used
--
2,400 h
Training time
--
$31M
Training cost (2023 USD)
--
38.4 MW
Training power draw
--
API access
Accessibility
Open weights (restricted use)
No
Open weights
Yes
United States
Country
United States
Related comparisons
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.