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Moshi by Kyutai
Audio AI

Moshi by Kyutai

Real-time full-duplex conversational voice AI model with sub-200ms latency

4.9
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About Moshi by Kyutai

Moshi is an open-source real-time conversational voice AI foundation model developed by Kyutai, the non-profit AI research lab based in Paris. Engineered to revolutionize human-AI verbal communication, Moshi operates on a full-duplex architecture capable of listening, thinking, and speaking simultaneously with sub-200ms end-to-end latency. Unlike traditional voice assistants that chain separate Speech-to-Text (STT), Large Language Model (LLM), and Text-to-Speech (TTS) pipelines together, Moshi processes raw multi-stream audio natively as continuous speech tokens. This allows Moshi to understand emotional nuances, interrupt and be interrupted naturally, chuckle, whisper, and express genuine conversational timing. Moshi is fully open-source with openly accessible weights, training recipes, and inference code, serving as a foundational milestone for research in real-time spoken language modeling and multi-modal conversational systems.

Moshi’s architecture is built on Helium, a 7-billion parameter language model coupled with Mimi, a cutting-edge neural audio codec that compresses 24kHz audio into multi-stream discrete tokens at just 1.1 kbps. By operating on a joint text-audio token stream, Moshi predicts both conversational text tokens and acoustic speech tokens in parallel. This end-to-end audio modeling eliminates the latency bottlenecks and acoustic information loss inherent in cascading STT-LLM-TTS pipelines. The Kyutai team provides full PyTorch and Rust-based inference engines optimized for local GPU execution, enabling real-time full-duplex conversations on consumer-grade hardware (NVIDIA RTX 4090 or Apple Silicon Mac).

How It Works
1

Visit the Moshi interactive chat demo or clone the official Kyutai GitHub repository.

2

Speak directly into your microphone in natural conversational flow.

3

The Mimi neural codec encodes your voice stream into discrete acoustic tokens.

4

The Helium 7B model processes audio tokens and generates simultaneous response speech tokens.

5

Moshi replies with natural emotional prosody and adapts instantly if you interrupt.

Platforms
WebAPI
Best For
AI ResearchersVoice EngineersDevelopersRobotics BuildersAudio Technologists
Screenshot
Moshi by Kyutai screenshot

Capabilities & Features

Free Tier
API Access
No Signup Required
Open Source
Works Offline
Customizable
Multimodal
Voice Input
Audio Output
White Label
Self-Hostable
Image InputImage OutputVideo InputVideo OutputFile UploadWeb SearchCode ExecutionPluginsMemoryCollaborationBrowser Extension

Common Use Cases

1

Sub-200ms Real-Time Conversational Voice AI Interaction

2

Full-Duplex Speech Research with Natural Interruption Handling

3

Human-Like Emotional Voice Avatars and Robotics Interfaces

4

Local Voice-Driven Assistant Execution on Consumer GPUs

5

Spoken Language Modeling and Codec Research

Frequently Asked Questions

What is Moshi by Kyutai?

Moshi is an open-source real-time conversational voice AI model that speaks and listens simultaneously with natural emotional intonation and sub-200ms latency.

What does 'full-duplex' mean in voice AI?

Full-duplex means the AI can listen to user speech while speaking at the same time, enabling natural interruptions and fluid human-like conversation without awkward delays.

Can I run Moshi locally on my own computer?

Yes, Kyutai provides full open-source weights and lightweight Rust/PyTorch inference runners that run on NVIDIA RTX 4090 GPUs or Apple Silicon Macs.

Pricing Modelfree

Free Plan

100% free and open-source under a permissive research and commercial license. Free online interactive demo available on Moshi chat.

Paid Plan

No paid tiers. Fully open model weights and code for community deployment.

Get Started

Direct link · Verified & reader-supported

Pros & Cons

World-first open-source full-duplex voice foundation model with sub-200ms response latency

Listens and speaks simultaneously, allowing natural interruptions and conversational pacing

Expresses genuine emotional nuance including whispers, laughter, and tone modulation

Native end-to-end audio modeling eliminating cascading STT-LLM-TTS latency bottlenecks

Completely open-source with PyTorch and Rust inference code available on GitHub

Runs locally on consumer hardware including single RTX 4090 GPUs and Apple Silicon Macs

Currently optimized primarily for conversational English with ongoing research in other languages

Requires high-performance GPU compute for low-latency local inference

2026 Migration & Procurement Guide

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