Running a large language model is expensive, and a surprising amount of that cost comes down to memory, not computation.

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for Apple Silicon and llama.cpp.

Part 2 looks at the tradeoffs between program and data cache optimizations, and shows how to choose the best compromise. As we saw in the first two parts of this series, cache optimization is often ...

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...