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Grok-2 vs U-PaLM (540B)

xAI
Grok-2
August 2024
vs
Google
U-PaLM (540B)
October 2022
3×10²⁵Training compute (FLOP)2.5×10²⁴
$32MTraining cost--

Grok-2 (xAI) and U-PaLM (540B) (Google) are both frontier AI models. Grok-2 was published in August 2024 and U-PaLM (540B) in October 2022.

Grok-2 was trained on 3×10²⁵ FLOP, about 11.7x 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
xAI
Organization
Google
Aug 13, 2024
Published
Oct 20, 2022
3×10²⁵ FLOP
Training compute12x
2.5×10²⁴ FLOP
--
Parameters
540B
--
Dataset size
1.3B
NVIDIA H100 SXM5 80GB
Training hardware
Google TPU v4
--
Chips used
512
--
Training time
120 h
$32M
Training cost (2023 USD)
--
--
Training power draw
348.3 kW
API access
Accessibility
Unreleased
No
Open weights
No
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.