Groq
Inference on purpose-built LPU hardware, sold as an API where the main feature is how little you wait.
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Link checked 19 September 2026: this site responded and still names the product.
About Groq
What it is
Groq runs open models on processors it designed specifically for inference rather than training, and sells access as an API with an OpenAI-compatible interface. It also operates its own data centre capacity rather than reselling somebody else's.
Why it's different
Everything here is one bet: that inference, not training, is where the demand and the bottleneck now sit, and that hardware built for it beats hardware adapted to it. The observable result is latency low enough to change how an application feels, particularly anything conversational or agentic where delays compound across calls. The constraints are a curated model list and dependence on a single supplier's capacity, which is a real consideration for anything you cannot afford to have queue.
How people use it
Voice and conversational applications where latency is the product. Agent loops making many sequential calls. Anything where a response should feel instant. Keep a second provider configured behind the same interface, because compatibility makes that cheap and capacity constraints make it worth having.
Written by the n3os team. We are not affiliated with Groq.
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