#inference

NVIDIA PAIR can turn the PCs you already own into a local AI cluster. But there’s a catch: it won’t pool VRAM. So when does it beat buying one bigger GPU, and when does it not? Read the full article on

the goal posts keep moving, maybe that fast nice new car ain’t work the $60k+ you was gonna drop on it when you start seeing piece of beast equipment like this. i’m certain that you could take this kind

Demand for AI has been skyrocketing ever since ChatGPT debuted in late 2022. In fact, the demand has been so great that we are facing several challenges... In addition to boatloads of additional energy,

I've been learning about using AI locally for a few months now. First, I learned about Quantization, Llamacpp and the GGUF format. I managed to get some models run on my Steam Deck, though heavily quantized

Aethir is a Decentralized Physical Infrastructure Network (DePIN) focused on making compute more accessible, scalable, and cost-effective for enterprises. Founded in 2021, Aethir has been using token

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How can Web 3.0 ever hope to advance if it is dependent upon centralized data? This is something that goes against the core of what is sought. We long discussed Web 3.0 and the challenges it faces. The

X and xAI are building a $750 million data center in the Atlanta area. This will comprise 12,448 A100 and H100 GPUs. It is a much smaller number than the Memphis facility leading me to believe it will

WIth the release of Deepseek, the markets were shook to the core. Stocks such as Nvidia were slaughtered, a massive sell off fueled by the belief that billions of dollars were being wasted on GPUs. Many

The tendency in the AI game is to look at the GPUs for model training. This is what caused Deepsake's announcement of more efficiency to shock the markets. In this video I discuss how Tesla is positioning

There is a lot going for Hive (and some exterior parts of the ecosystem) that make it ideal for the democratization of the AI stack. In this video I discuss the components of democratized data, no direct

Relying on mega-corporations like OpenAI, Google and Microsoft to help you with AI tasks isn't risk-free. Among other drawbacks, these tech giants could suspend your account, censor your results, or even

Jensen Huang, the CEO of NVIDIA, stated that inference is going to be a billion times larger than training compute in the future. Let that statement sink in. This is coming from the head of the leading

We are seeing the cost of inference computing dropping very rapidly. We have projections where ChatGPT 4.0 quality could be attained on a smartphone by the end of 2026. In this video I discuss how this

Over the last couple quarters, the discussion around inference computing is heightening. As AI models become more popular, the need for inference explodes. In this video I discuss how this was mentioned

Compute is going to be the currency of the future, at least for major businesses. We see how this is already forming. In this video I discuss a concept such as distributed computing, and how crucial it

There is a lot of focus on companies that are achieving GPUs like Meta, Xai, and Tesla. These are obviously required for the training of neural networks. In this video I discuss how, over the next few