Groq is what happens when a hardware company optimizes for LLM inference latency from first principles. The custom LPU architecture delivers token-generation speeds that are visibly faster than GPU-based competitors — often 5-10× faster on the same model — which fundamentally changes what kinds of applications are possible. Real-time voice agents, instant-feeling chat UIs, and streaming use cases that feel sluggish on other infrastructure feel snappy on Groq. The free tier is generous enough for serious prototyping and small production use; paid tiers scale with volume. The trade-off versus Together and Replicate is model selection — Groq hosts a curated set of open models rather than the long tail. For teams whose use case is latency-sensitive, the speed difference is qualitative, not just quantitative; for teams with production volume on a specific supported model, the cost economics also tend to be competitive.
Groq is an LLM inference provider running on its custom-designed Language Processing Units (LPUs). Generates output token streams at speeds (often 500-1000+ tokens/second) significantly faster than GPU-based competitors. Hosts open-source models like Llama, Mixtral, Whisper, and Qwen.