First, Let’s Clear Up the Groq Confusion
If you’ve heard “Grok” thrown around lately, you’re probably thinking of Elon’s chatbot from xAI. We’re not talking about that one. That model isn’t particularly good, but its whole value prop is being politically incorrect so you can get it to say edgy things.
The company Nvidia bought is Groq (with a Q). Totally different beast.
What Groq Actually Does
If you’ve used any high quality LLM, you’ve noticed it takes a while to generate a response. Especially for something rapid fire like a conversation, you want high quality AND speed. But speed is often what gets sacrificed. There’s always that “thinking... gathering my notes... taking some time to form the best response” delay.
My default Zellij layout so I can parallelize Claude Code tasks.
Groq specialized in hardware and software that makes this way faster. They created a new type of chip called an LPU (Language Processing Unit). It’s based on an ASIC, an application specific integrated circuit. If that’s confusing, don’t worry about it. It’s just a processor that does a specific type of task really well.
So imagine you’re talking to Gemini and it takes a couple seconds to respond. Now imagine it responded instantly, like 10 or 100 times faster. That’s the problem Groq was solving.
I Explain LPUs vs GPUs So That Anyone Can Understand Them In One Minute
To go one level deeper on LPUs versus GPUs (the processors most LLMs run on, typically Nvidia cards): those GPU calculations have to access a lot of things in memory. Nvidia’s chips depend on HBM, high bandwidth memory. But LPUs use something called SRAM that’s much faster to reference.
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