<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
Why are GPUs better than CPUs at performaing machine learning tasks?<|im_end|>
<|im_start|>assistant
Unlike CPUs, GPUs are designed to be able to perform operations with very high degree of parallelism, thanks to hundreds of processing units (at the cost of lesser capabilities of each unit). If a task is about manipulating large arrays of numbers, which is often the case in both 3D graphics and neural or networks, a GPU may complete it much faster than a CPU.<|im_end|>
<|im_start|>user
Ia holographic memory useful for AI and AGI?<|im_end|>
<|im_start|>assistant
Yes, holographic memory is a promising technology for AI and AGI. Holographic memory is a type of non-volatile memory that can store large amounts of data in a small space. It is also very fast and energy-efficient. These properties make it ideal for AI and AGI applications, which require large amounts of data to be processed quickly and efficiently.

Holographic memory works by storing data in a three-dimensional pattern. This allows it to store much more data than traditional two-dimensional memory technologies. Holographic memory is also very fast because it can access data in parallel. This means that it can read and write data from multiple locations at the same time.

Holographic memory is still under development, but it has the potential to revolutionize AI and AGI. It could enable AI systems to learn and process information much faster than they can today. This could lead to major breakthroughs in areas such as natural language processing, computer vision, and robotics.<|im_end|>
<|im_start|>user
Would a CPU be completely insufficient for running a localized AI?<|im_end|>
<|im_start|>assistant
No, there is some effort to optimize and adjust LLMs to running on CPU more efficiently. 

For instance, LLaMA has been successfully ported to C/C++ ([see github link](https://github.com/ggerganov/llama.cpp)) in Spring 2023, and with some modifications to memory management can allegedly run on a Mac with an output speed in the order of seconds per token. This is not enough for a fluent conversation, but will work for processing and summarizing batches of documents, for example. And this is just one example, the work is surely going on!<|im_end|>