SenseChat (SenseTime) and U-PaLM (540B) (Google) are both frontier AI models. SenseChat was published in April 2023 and U-PaLM (540B) in October 2022.
SenseChat was trained on 3.9 x 10^24 FLOP, about 1.5x the compute of U-PaLM (540B) at 2.5 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 | SenseChat | U-PaLM (540B) |
|---|---|---|
| Organization | SenseTime | |
| Published | Apr 10, 2023 | Oct 20, 2022 |
| Training compute | 3.9 x 10^24 FLOP | 2.5 x 10^24 FLOP |
| Parameters | 180B | 540B |
| Dataset size | — | 1.3B |
| Training hardware | — | Google TPU v4 |
| Chips used | — | 512 |
| Training time | — | 120 h |
| Training cost (2023 USD) | $5M | — |
| Training power draw | — | 348.3 kW |
| Accessibility | API access | Unreleased |
| Open weights | No | No |
| Country | Hong Kong | United States |
