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It would be interesting to benchmark a short training / inference run on the latest of TPU vs. NVIDIA GPU per cost basis

Splitting TPUs into dedicated training vs inference chips feels like an admission that the bottleneck has shifted from FLOPs to memory bandwidth + latency. Are future gains to come more from memory/system design than raw compute scaling? What’s that saying about Scaling laws?

Thank you for your kind words. Great catalog. I found WordFor already.

I first compute the embeddings to the dictionaries and store these. At query-time, when the user inputs the description, its embeddings are again computed by a lighter model. Then comparison happens between this and the stored ones

The comparison to automobiles changing streets is thrown around a lot. But I feel AI is fundamentally different. It is not a technological change like the internet which brought us huge amounts of opportunities in so many different directions. AI’s goal is to automate (in other words, replace) us.

Sorry, how did you calculate the 10.25B?

Got it. Thanks

~I see. What’s the 6?~

Nevermind, the other reply clears it

What do all the numbers 6-35B-A3B mean?