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Groq Inference Tokenomics: Speed, But At What Cost?

semianalysis.com · 1,713 words · saved by 2 readers

Groq, an AI hardware startup, has been making the rounds recently because of their extremely impressive demos showcasing the leading open-source model, Mistral Mixtral 8x7b on their inference API. They are achieving up to 4x the throughput of other inference services while also charging less than 1/3 that of Mistral themselves. Groq has a genuinely amazing performance advantage for an individual sequence. This could enable techniques such as chain of thought to be far more usable in the real world. Furthermore, as AI systems become autonomous, output speeds of LLMs need to be higher for applications such as agents. Likewise, codegen also needs token output latency to be significantly lower as well. Real time Sora style models could be an incredible avenue for entertainment. These services may not even be viable or usable for end market customers if the latency is too high. This has led to an immense amount of hype regarding Groq’s hardware and inference service being revolutionary for

Groq Inference Tokenomics: Speed, But At What Cost? Faster than Nvidia? Dissecting the economics Dylan Patel and Daniel Nishball Feb 21, 2024 ∙ Paid 134 5 Share Groq, an AI hardware startup, has been making the rounds recently because of their extremely impressive demos showcasing the leading open-source model, Mistral Mixtral 8x7b on their inference API . They are achieving up to 4x the throughput of other inference services while also charging less than 1/3 that of Mistral themselves. https://artificialanalysis.ai/models/mixtral-8x7b-instruct Groq has a genuinely amazing performance advantag

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