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Head to head

Minerva (540B) vs PaLM (540B)

Google
Minerva (540B)
June 2022
vs
Google Research
PaLM (540B)
April 2022
2.7×10²⁴Training compute (FLOP)2.5×10²⁴
--Training cost$3M

Minerva (540B) (Google) and PaLM (540B) (Google Research) are both frontier AI models. Minerva (540B) was published in June 2022 and PaLM (540B) in April 2022.

Minerva (540B) was trained on 2.7×10²⁴ FLOP, essentially the same compute as 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
Google
Organization
Google Research
Jun 29, 2022
Published
Apr 4, 2022
2.7×10²⁴ FLOP
Training compute1.1x
2.5×10²⁴ FLOP
540.4B
Parameters
540.4B
26B
Dataset size30x
780B
Google TPU v4
Training hardware
Google TPU v4
1,024
Chips used6.0x
6,144
696 h
Training time
1,536 h
--
Training cost (2023 USD)
$3M
698.4 kW
Training power draw
4.2 MW
Unreleased
Accessibility
Unreleased
No
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.