Groq runs open-weight models on custom LPU hardware at throughput GPUs do not reach. On a voice call that translates into one thing you can actually hear: the agent starts replying almost as soon as you stop talking.
Groq is an inference provider that runs models on Language Processing Units — purpose-built silicon for sequential token generation rather than general-purpose GPUs. Depending on the model it serves between roughly 276 and 1,500+ tokens per second, against the 40–100 typical of GPU-based providers. It hosts open-weight models including Llama 4 and Llama 3.x, Mixtral, Gemma, DeepSeek R1 distills and Whisper.
In a voice conversation, the model's generation speed is not an abstraction — it is the length of the silence after the caller stops talking. A model that generates twice as fast halves that pause. Groq is the clearest lever available on that number, which is why it is the usual choice for agents where responsiveness beats reasoning depth.
What you get, stated plainly enough to check.
Four steps, none of which involve writing telephony code.
Select an open-weight model served on Groq as your agent's brain. Vomyra handles the routing and failover.
Groq removes the think-time; Cartesia removes the speak-time. Together they produce the tightest turn in the stack.
Fast inference rewards concise system prompts. Long, branching prompts spend the latency budget Groq just gave you back.
Vomyra reports end-to-end turn latency per call, which is the number that actually correlates with call quality.
Generation speed is the silence after the caller stops. Groq is the most direct lever on it available anywhere.
Open-weight models on fast hardware keep cost per call low at volume, without the per-token pricing of frontier proprietary models.
Groq for thinking, Cartesia for speaking, Deepgram for hearing, your carrier for reaching. Each layer is chosen independently.
It runs the reasoning model on custom LPU hardware that generates tokens substantially faster than conventional GPU inference. On a phone call that shows up as a shorter silence between the caller finishing and the agent starting to reply.
No, and they are constantly confused. Groq is an inference hardware and cloud provider that serves open-weight models very fast. Grok is xAI's family of language models. Both run on Vomyra, and you can use them together or separately.
Open-weight families including Llama 4 and Llama 3.x, Mixtral, Gemma and DeepSeek R1 distills, plus Whisper for speech. Vomyra exposes the supported ones as model choices on an agent and handles routing and failover behind the selection.
When the agent needs deep reasoning over long, policy-heavy scripts. Open-weight models on fast hardware trade some reasoning depth for speed. For complex compliance-bound conversations, a frontier model like Claude or GPT-4.1 is usually the better call.
That is the normal setup. Groq for inference, Cartesia or ElevenLabs for the voice, Deepgram for transcription where a classic pipeline is used, and your own carrier or a Vomyra number for telephony. Each layer is chosen independently.