EXAONE 1.0 (LG) and Flan-PaLM 540B (Google) are both frontier AI models. EXAONE 1.0 was published in December 2021 and Flan-PaLM 540B in October 2022.
Flan-PaLM 540B was trained on 2.5 x 10^24 FLOP, about 1.5x the compute of EXAONE 1.0 at 1.7 x 10^24 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.
| Field | EXAONE 1.0 | Flan-PaLM 540B |
|---|---|---|
| Organization | LG | |
| Published | Dec 14, 2021 | Oct 20, 2022 |
| Training compute | 1.7 x 10^24 FLOP | 2.5 x 10^24 FLOP |
| Parameters | 300B | 540B |
| Dataset size | — | 1.4B |
| Training hardware | — | Google TPU v4 |
| Chips used | — | 512 |
| Training time | — | 37 h |
| Training cost (2023 USD) | $3M | — |
| Training power draw | — | 348.3 kW |
| Accessibility | Unreleased | Unreleased |
| Open weights | No | No |
| Country | South Korea | United States |
