[Jlagreen]

That was never the moat.

Nvidia's moat is controlling compute for 80-90% of workloads.

Why do you think Steam has more RTX 4090 than 4080 in their survey?

Ever since RTX 2080TI you could buy multi server consumer GPUs for ML. We sell PCs with RTX cards regularly to customer for small local AI applications.

Project Digits is the next thing. It is not only a Nvidia GPU in some PC, it's the real AI PC. The real deal and we're already considering switchting to that as a system instead of a PC since it's size is perfect for our Vision application.

Do you think Nvidia cares if you buy 1 Blackwell DC GPU, 13x Digits or 20x RTX 5090? In the end it's all the same turnover for Nvidia.

Nvidia's goal is to spread and dominate workloads worldwide and that no matter if DC, enterprise or consumer, Nvidia HW is used.

[poobear22]

I re-read the thread topic and it is in response to NVIDIA stock. My response was in response to LLM generators and their moat (not NVIDIA's moat). So, my point was the moat for LLM generators had diminished. Additional competition generating LLMs from new entrants may increase demand for HW. The HW may be used more efficiently but I am still waiting to see if LLM performance continues to improve.

[Jlagreen]

Ah, I'm sorry you're right a misunderstood your comment.

I agree and Nvidia positions itself for exactly that. See how fast DeepSeek will come to NIM. People are already wondering how well DeepSeek will run on Digits.

Nvidia also offers distilled open models or specific own open models so is indirectly competing in that space as well. But Nvidia isn't in the LLM generator business but in the business of "infrastructure for LLM generators"

Everyone is waiting for GPT5 or another big bang. And because it takes so much people start to think that there is a wall. And there is a wall but that wall could be also compute. Blackwell will show if there is a compute wall because simply put, if a training run with large parameter set on GPT5 takes like 4 months then Blackwell might reduce that to under 1 month with the same amount of GPUs. Getting more GPUs can get that down even more. Imagine the speed up in AI frontier model research if your training times come down 4-5x from new GPU generation and another 2x from getting twice as many.

The nice part with Nvidia is also that the old GPUs don't become obsolete, OpenAI can continue using them for inferencing or even try to use combined architecture training as long as they don't go FP4.

I wouldn't be surprised that at the end of 2025 we will see things which will make DeepSeek and GPT4 as oldschool stuff simply because of the massive compute which Blackwell will deliver this year.