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Might be a bit out of context, but isn't the TPU also optimized for low latency inference? (Judging by reading the original TPU architecture paper here - https://arxiv.org/abs/1704.04760). If so, does Groq actually provide hardware support for LLM inference?

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Jonathan Ross on that paper is Groq's founder and CEO. Groq's LPU is an natural continuation of the breakthrough ideas he had when designing Google's TPU.

Could you clarify your question about hardware support? Currently we build out our hardware to support our cloud offering, and we sell systems to enterprise customers.

Thanks for the quick reply! About hardware support, I was wondering if the LPU has a hardware instruction to compute the attention matrix similar to the MatrixMultiply/Convolve instruction in the TPU ISA. (Maybe a hardware instruction which fuses a softmax on the matmul epilogue?)

We don't have a hardware instruction but we do have some patented technology around using a matrix engine to efficiently calculate other linear algebra operations such as convolution.

Are you considering targeting consumer market? There are a lot of people throwing $2k-$4k into local setups and they primarily care about inference.

At the moment we're concentrating on building out our API and serving the enterprise market.

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