Groq runs open models on custom hardware at speeds that change what real-time AI can do. Here's what it is, and where that speed is worth designing around.
Written forEngineeringProductFounders & Business
GroqInferenceLatency
Most inference providers compete on model selection or price. Groq competes on raw speed. By running open models on its own custom hardware (LPUs, not GPUs), it serves tokens fast enough that latency stops being a constraint you design around and becomes a feature you can build on.
What makes it different
The architecture is purpose-built for inference, which translates to very high tokens-per-second and low, predictable latency. It hosts popular open models and exposes an OpenAI-compatible API, so adopting it is usually a base-URL and API-key change rather than a rewrite.
Where speed is genuinely the feature
Voice and real-time agents — where every hundred milliseconds is the difference between a conversation and an awkward pause.
Multi-step workflows — when an agent chains ten model calls, per-call latency compounds; fast inference keeps the whole chain snappy.
Interactive UX — instant, streaming responses that feel alive rather than loading.
High-volume classification & routing — throughput that lets you run a model on traffic you'd otherwise have to sample.
The trade-offs
Model selection is limited to what Groq hosts — you won't find every proprietary frontier model there.
For the hardest reasoning tasks, you may still want a top proprietary model and trade some speed for quality.
Speed removes one constraint; it doesn't remove the need for evals, guardrails, and good prompt design.
Adopting it is mostly a base-URL change
const client = new OpenAI({
apiKey: process.env.GROQ_API_KEY,
baseURL: 'https://api.groq.com/openai/v1',
});
// same calls, dramatically faster tokens
When the model is fast enough to feel instant, you stop hiding latency and start designing around its absence.
Building something with LLMs?
I help teams ship GenAI that’s reliable and cost-efficient.