Three language model ports caught my attention because they take very different approaches to hardware constraints: Commodore 64: llama2.c64 runs a tiny 260K parameter story model and requires at least a 2 MB RAM Expansion Unit. The extra memory is an essential part of the setup. DOS: dosllam2 uses a 32 bit DOS extender to access memory beyond the conventional DOS limit. Its author reports 2.08 tokens per second for the 260K model on a 66 MHz 486 with 32 MB RAM. Nintendo Wii: iiama2.c runs stories15M with int8 weights. Its GPU implementation repurposes the console’s fixed function texture combiner for multiplication and accumulation, while operations such as attention remain on the CPU. These examples show why the checkpoint, memory expansion and numerical precision matter when describing what a device can run. The smallest models here generate simple story continuations, with capabilities that reflect their size and training. I’m maintaining a collection of these ports , recording hardware requirements, model details and author reported speeds. The original authors did the porting work. Are there other documented examples where someone repurposed an unusual hardware feature for language model inference? The Wii’s texture combiner approach is the kind of detail I’d like to find more of. submitted by /u/Awacer
Originally posted by u/Awacer on r/ArtificialInteligence