Why is there a limit on the number of tokens in transformers?

The token limit in transformers is related to the architectural and computational features of the model:

• Computational complexity: Attention in transformers has quadratic complexity with respect to sequence length (O(n²)), as each token interacts with every other. The more tokens, the more memory and time are required.

• Memory constraints: Long sequences require a lot of RAM, which limits the maximum input length.

• Training and generalization: The model is trained on a limited sequence length, and exceeding these limits can degrade prediction quality.

• Practical reasons: The limit helps control latency and resource usage when operating the model in real-time.

Thus, the token limit is a compromise between performance, quality, and resources.