EXAONE 1.0 (LG) and PaLM 2 (Google) are both frontier AI models. EXAONE 1.0 was published in December 2021 and PaLM 2 in May 2023.
PaLM 2 was trained on 7.3 x 10^24 FLOP, about 4.3x 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 | PaLM 2 |
|---|---|---|
| Organization | LG | |
| Published | Dec 14, 2021 | May 10, 2023 |
| Training compute | 1.7 x 10^24 FLOP | 7.3 x 10^24 FLOP |
| Parameters | 300B | 340B |
| Dataset size | — | 3.6T |
| Training hardware | — | Google TPU v4 |
| Training cost (2023 USD) | $3M | $5M |
| Accessibility | Unreleased | API access |
| Open weights | No | No |
| Country | South Korea | United States |
