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Nemotron-4 340B vs U-PaLM (540B)

NVIDIA
Nemotron-4 340B
June 2024
vs
Google
U-PaLM (540B)
October 2022
1.8×10²⁵Training compute (FLOP)2.5×10²⁴
$21MTraining cost--

Nemotron-4 340B (NVIDIA) and U-PaLM (540B) (Google) are both frontier AI models. Nemotron-4 340B was published in June 2024 and U-PaLM (540B) in October 2022.

Nemotron-4 340B was trained on 1.8×10²⁵ FLOP, about 7.1x the compute of U-PaLM (540B) at 2.5×10²⁴ 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
NVIDIA
Organization
Google
Jun 14, 2024
Published
Oct 20, 2022
1.8×10²⁵ FLOP
Training compute7.1x
2.5×10²⁴ FLOP
340B
Parameters1.6x
540B
9T
Dataset size6923x
1.3B
NVIDIA H100 SXM5 80GB
Training hardware
Google TPU v4
6,144
Chips used12x
512
2,200 h
Training time
120 h
$21M
Training cost (2023 USD)
--
8.5 MW
Training power draw
348.3 kW
Open weights (unrestricted)
Accessibility
Unreleased
Yes
Open weights
No
United States
Country
United States
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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.