111–120 of 197 posts
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> There are many ways to monetize a chatbot, OpenAI for example is raking billions in subscription fees. Compared to Google, OpenAI's billions is peanuts, while costing a fortune to generate. GPT-4 doesn't seem profitable (if it was, would they need to throttle it?)
Wouldn't Google be better able to integrate ads into a "ChatGoogle" service than OpenAI is into ChatGPT?
On the podcast interview now Groq CEO Jonathon Ross did[1] he talked about the creation of the original TPUs (which he built at Google). Apparently originally it was a FPGA he did in his 20% time because he sat near the team who was having inference speed issues. They got it working, then Jeff Dean did the math and the decided to do an ASIC. Now of course Google should spin off the TPU team as a separate company. It'…
The way I see, NVidia only has a few advantages ordered from most important to least: 1. Reserved fab space. 2. Highly integrated software. 3. Hardware architecture that exists today. 4. Customer relationships. but all of these aspects are weak in one way or another: For #1, fab space is tight, and NVidia can strangle its consumer GPU market if it means selling more AI chips at a higher price. This advantage is gone…
NVidia's software is the only reason I'm not using GPU's for ML tasks and likely never will.
How is it that Google invented the TPU and Google Research came up with the paper on LLM and NVDA and AI startup companies have captured ~100% of the value
https://www.linkedin.com/posts/eolver_googles-defense-agains...
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> It's the only credible competition NVidia has This is wrong, both AMD and Intel (through Habana) have GPUs comparable to H100s in performance.
Yes, but they don't have the custom kernels that CUDA has. TPUs do have some!
And AMD has ROCm. pytorch is standard and pytorch has ROCm support. And the Google TPU v5 also has pytorch support.
We do have a couple of H100's, but I'd love to replace them with AMD's
How is it that Google invented the TPU and Google Research came up with the paper on LLM and NVDA and AI startup companies have captured ~100% of the value
It's far too early to suggest Google will not capture value from AI. They have plenty of opportunity to integrate AI into their products.
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The way I see, NVidia only has a few advantages ordered from most important to least: 1. Reserved fab space. 2. Highly integrated software. 3. Hardware architecture that exists today. 4. Customer relationships. but all of these aspects are weak in one way or another: For #1, fab space is tight, and NVidia can strangle its consumer GPU market if it means selling more AI chips at a higher price. This advantage is gone…
CUDA is absolute shit, segfaults or compiler errors if you look at it wrong. NVidia's software is the only reason I'm not using GPU's for ML tasks and likely never will.
Can't blame CUDA for that one.
On the podcast interview now Groq CEO Jonathon Ross did[1] he talked about the creation of the original TPUs (which he built at Google). Apparently originally it was a FPGA he did in his 20% time because he sat near the team who was having inference speed issues. They got it working, then Jeff Dean did the math and the decided to do an ASIC. Now of course Google should spin off the TPU team as a separate company. It'…
The way I see, NVidia only has a few advantages ordered from most important to least: 1. Reserved fab space. 2. Highly integrated software. 3. Hardware architecture that exists today. 4. Customer relationships. but all of these aspects are weak in one way or another: For #1, fab space is tight, and NVidia can strangle its consumer GPU market if it means selling more AI chips at a higher price. This advantage is gone…
Earlier quoted context omitted.
> Now of course Google should spin off the TPU team as a separate company. Given the size of the market and its near-monopoly situation, I strongly think this has the potential to (almost immediately) surpass the Pixel hardware business. But the problem here is that TPU is a relatively scarce computing resource even inside Google and it's very likely that Google has a hard time to meet its internal demands...
I’m surprised they sell any to external customers, to be honest.
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Wouldn't Google be better able to integrate ads into a "ChatGoogle" service than OpenAI is into ChatGPT?
The cost per ad is still astronomically different between search ads and LLMs
> However, although tensors describe the relationship between arbitrary higher-dimensional arrays, in practice the TPU hardware that we will consider is designed to perform calculations associated with one and two-dimensional arrays. Or, more specifically, vector and matrix operations. I still don’t understand why the term “tensor” is used if it’s only vectors and matrices.
Nevertheless, I have always interpreted it as a reference to the fact that the optimal method of multiplying matrices is to decompose the matrix multiplication into tensor products of vectors.
The other 2 alternative methods, i.e. decomposing the matrix multiplication into scalar products of vectors or into AXPY operations on pairs of vectors, have a much worse ratio between computation operations and transfer operations.
Unfortunately, most people learn in school the much less useful definition of the matrix multiplication based on scalar products of vectors, instead of its definition based on tensor products of vectors, which is the one needed in practice.
The 3 possible methods for multiplying matrices correspond to the 6 possible orders for the 3 indices of the 3 nested loops that compute a matrix product.