Except LLMs tend to be very big compared to standard decompression programs and often requires GPU with adequate VRAM in order to work reasonably fast enough. This is a very big usability issue IMO. If decompression can be done with a smaller and faster program (maybe also generated by the LLM?), it can be very useful and see pretty wide adoption (e.g. for future game devs who want to reduce their game size from 150GB to 130GB).
Training tends to be more compute intensive while inference is more likely to be able to be ran on a smaller hardware foot print.
The neater idea would be a standard model or set of models, so that a 30G program can be used on ~80% of target case, games and video seem good canidates for this.
Except LLMs tend to be very big compared to standard decompression programs and often requires GPU with adequate VRAM in order to work reasonably fast enough. This is a very big usability issue IMO. If decompression can be done with a smaller and faster program (maybe also generated by the LLM?), it can be very useful and see pretty wide adoption (e.g. for future game devs who want to reduce their game size from 150GB to 130GB).
Training tends to be more compute intensive while inference is more likely to be able to be ran on a smaller hardware foot print.
The neater idea would be a standard model or set of models, so that a 30G program can be used on ~80% of target case, games and video seem good canidates for this.