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Really glad you like it! We've been working hard on it.


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The speed part or the being swallowed part?

The speed part. We're not interested in being swallowed. The aim is to be bigger than Nvidia in three years :)

Go for it!

Can you warn us pre-IPO?

I'm sure you'll hear all about our IPO on HN :) :)

Yes please

Is Sam going to give you some of his $7T to help with that?

Why wouldn't NVIDIA release their own LPU?

Is this useful for training as well as running a model. Or is this approach specifically for running an already-trained model faster?

In principle, training is basically the same as running inference but iteratively, in practice training would use a different software stack.

Training requires a lot more memory to keep gradients + gradient stats for the optimizer, and needs higher precision weights for the optimization. It's also much more parallelizable. But inference is kind of a subroutine of training.

Currently graphics processors work well for training. Language processors (LPUs) excel at inference.

Did you custom build those Language processors for this task? Or did you repurpose something already existing? I have never heard anyone use ‘Language processor’ before.

The chips are built for general purpose low latency, high throughput numerical compute.

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