Meta Byte Latent Transformer is Another Way Inference Costs Will Keep Dropping

Large Language Model costs are going to keep dropping. DeepSeek was only one example. Now Meta developers propose to use Byte Latent Transformer (BLT) as a new alternative to using tokens for language models.

Meta has introduced the Byte Latent Transformer (BLT) as a new alternative to using tokens for language models.   

Instead of breaking down text into tokens, BLT processes data directly at the byte level. This allows the model to handle any language or data format without needing predefined vocabularies.   

Some potential benefits include lower inference cost, as noisy or non-standard text (text with typos, mixed languages, or special characters) can be processed more efficiently. 

BLT also is said to dynamically group bytes into "patches," potentially reducing computational effort (and hence cost of inferences). 

BLT's tokenizer-free approach could make it easier to develop models for languages with limited data.

The point is that AI language model costs are going to keep dropping. As that happens, we will see greater usage across a wider range of applications and processes.