Since the models are not suppressed to answer certain questions and indeed have been demonstrated to be biased toward one end of the political spectrum, and if you treat the model’s output as a knowledge graph, as proposed by John Schulman, one of the cofounders of OpenAI, then yes, I would say the suppression of freedom of speech is a valid argument to make. Otherwise why would there be a set of “uncensored” models that exist in the open source world?
I would suggest you read about these models and think about the implications. Perhaps that’ll lead to you to reconsider your stance
I have done that, and come to the conclusion that "that's business, baby!"
Do you have any more meaningful objections to this "censorship" of a free thing you agreed to license?
NVIDIA never sold a GPU with expandable memory, either.
The PC gamers really want the GPU component to be separately upgradable from the CPU. Non-gamer PC users don't care about the GPU performance, just cost. The datacenter folks want to be able to use a single $1k CPU to host $100k worth of GPUs.
It's plausible that AI accelerators follow a different path for the consumers. It's harder to see it happening for the datacenter market.
Being able to switch from an optimized CPU-centric workload to an optimized GPU-centric workload without any hardware changes sounds useful to me.
You could even do unified memory with upgradeable separate CPU and GPU. You won’t get the benefits of having them on the same chip, but there’s nothing intrinsic about the separation requiring separate memory space.
A100 goes up to 2TB/s in the largest configuration, and H100 claims 3TB/s per GPU node. (These figures keep changing with new variants.) But you can buy several Mac Studios for the price :-)
The real use of H100 is for training, as it can pool many GPUs together with a dedicated high bandwidth network. You can't do that with Mac Studios.
As LLM adoption grows, I wonder whether Apple's approach will start to make more sense for consumer adoption, so that you can run models on your machine without needing to pay large subscription costs for AI-powered apps (since OpenAI et al have fairly high fees). The high cost of using the APIs in my opinion is a drag on certain types of adoption in consumer apps.
Llama.cpp actually first started as a way to run LLMs on Macs! At first CPU-only, but then later the first GPU driver backend added was Metal, not anything from Nvidia.