pcalau12i

The 1.5B/7B/8B/13B/32B/70B models are all officially DeepSeek R1 models, that is what DeepSeek themselves refer to those models as. It is DeepSeek themselves who produced those models and released them to the public and gave them their names. And their names are correct, it is just factually false to say they are not DeepSeek R1 models. They are.

The “R1” in the name means “reasoning version one” because it does not just spit out an answer but reasons through it with an internal monologue. For example, here is a simple query I asked DeepSeek R1 13B:

Me: can all the planets in the solar system fit between the earth and the moon?

DeepSeek: Yes, all eight planets could theoretically be lined up along the line connecting Earth and the Moon without overlapping. The combined length of their diameters (approximately 379,011 km) is slightly less than the average Earth-Moon distance (about 384,400 km), allowing them to fit if placed consecutively with no required spacing.

However, on top of its answer, I can expand an option to see its internal monologue it went through before generating the answer, which you can find the internal monologue here because it’s too long to paste.

What makes these consumer-oriented models different is that that rather than being trained on raw data, they are trained on synthetic data from pre-existing models. That’s what the “Qwen” or “Llama” parts mean in the name. The 7B model is trained on synthetic data produced by Qwen, so it is effectively a compressed version of Qen. However, neither Qwen nor Llama can “reason,” they do not have an internal monologue.

This is why it is just incorrect to claim that something like DeepSeek R1 7B Qwen Distill has no relevance to DeepSeek R1 but is just a Qwen model. If it’s supposedly a Qwen model, why is it that it can do something that Qwen cannot do but only DeepSeek R1 can? It’s because, again, it is a DeepSeek R1 model, they add the R1 reasoning to it during the distillation process as part of its training. (I think they use the original R1 to produce the data related to the internal monologue which it is learns to copy.)

As I said, they will likely come to the home in form of cloud computing, which is how advanced AI comes to the home. You can run some AI models at home but they’re nowhere near as advanced as cloud-based services and so not as useful. I’m not sure why, if we ever have AGI, it would need to be run at home. It doesn’t need to be. It would be nice if it could be ran entirely at home, but that’s no necessity, just a convenience. Maybe your personal AGI robot who does all your chores for you only works when the WiFi is on. That would not prevent people from buying it, I mean, those Amazon Fire TVs are selling like hot cakes and they only work when the WiFi is on. There also already exists some AI products that require a constant internet connection.

It is kind of similar with quantum computing, there actually do exist consumer-end home quantum computers, such as Triangulum, but it only does 3 qubits, so it’s more of a toy than a genuinely useful computer. For useful tasks, it will all be cloud-based in all likelihood. The NMR technology Triangulum is based on, it’s not known to be scalable, so the only other possibility that quantum computers will make it to the home in a non-cloud based fashion would be optical quantum computing. There could be a breakthrough there, you can’t rule it out, but I wouldn’t keep my fingers crossed. If quantum computers become useful for regular people in the next few decades, I would bet it would be all through cloud-based services.

If quantum computers actually ever make significant progress to the point that they’re useful (big if) it would definitely be able to have positive benefits for the little guy. It is unlikely you will have a quantum chip in your smartphone (although, maybe it could happen if optical quantum chips ever make a significant breakthrough, but that’s even more unlikely), but you will still be able to access them cheaply over the cloud.

I mean, IBM spends billions of on its quantum computers and gives cloud access to anyone who wants to experiment with them completely free. That’s how I even first learned quantum computing, running algorithms on IBM’s cloud-based quantum computers. I’m sure if the demand picks up if they stop being experimental and actually become useful, they’ll probably start charging a fee, but the fact it is free now makes me suspect it will not be very much.

I think a comparison can be made with LLMs, such as with OpenAI. It takes billions to train those giant LLMs as well and can only be trained on extremely expensive computers, yet a single query costs less than a penny, and there are still free versions available. Expense for cloud access will likely always be incredibly cheap, it’s a great way to bring super expensive hardware to regular people.

That’s likely what the future of quantum computing will be for regular people, quantum computing through cloud access. Even if you never run software that can benefit from it, you may get benefits indirectly, such as, if someone uses a quantum computer to help improve medicine and you later need that medicine.