Moshi by Kyutai
Real-time full-duplex conversational voice AI model with sub-200ms latency
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).
Visit the Moshi interactive chat demo or clone the official Kyutai GitHub repository.
Speak directly into your microphone in natural conversational flow.
The Mimi neural codec encodes your voice stream into discrete acoustic tokens.
The Helium 7B model processes audio tokens and generates simultaneous response speech tokens.
Moshi replies with natural emotional prosody and adapts instantly if you interrupt.
Capabilities & Features
Common Use Cases
Sub-200ms Real-Time Conversational Voice AI Interaction
Full-Duplex Speech Research with Natural Interruption Handling
Human-Like Emotional Voice Avatars and Robotics Interfaces
Local Voice-Driven Assistant Execution on Consumer GPUs
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.
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.
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
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