quickstart.mdx

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Core Models	Core Models	The three core components to Vapi's voice AI pipeline.

At it's core, Vapi is an orchestration layer over three modules: the transcriber, the model, and the voice.

These three modules can be swapped out with any provider of your choosing; OpenAI, Groq, Deepgram, ElevenLabs, PlayHT, etc. You can even plug in your server to act as the LLM.

Vapi takes these three modules, optimizes the latency, manages the scaling & streaming, and orchestrates the conversation flow to make it sound human.

When a person speaks, the client device (whether it is a laptop, phone, etc) will record raw audio (1’s & 0’s at the core of it).

This raw audio will have to either be transcribed on the client device itself, or get shipped off to a server somewhere to turn into transcription text.

That transcript text will then get fed into a prompt & run through an LLM ([LLM inference](/glossary#inference)). The LLM is the core intelligence that simulates a person behind-the-scenes.

The LLM outputs text that now must be spoken. That text is turned back into raw audio (again, 1’s & 0’s), that is playable back at the user’s device.

This process can also either happen on the user’s device itself, or on a server somewhere (then the raw speech audio be shipped back to the user).

The idea is to perform each phase in realtime (sensitive down to 50-100ms level), streaming between every layer. Ideally the whole flow voice-to-voice clocks in at <500-700ms.

Vapi pulls all these pieces together, ensuring a smooth & responsive conversation (in addition to providing you with a simple set of tools to manage these inner-workings).