Cartesia Deploys State Space Models on AWS to Deliver Ultra-Low-Latency Voice AI

Conversational AI is rapidly reshaping human-computer interaction, with voice-first interfaces emerging as the preferred engagement mode. Achieving near-human responsiveness remains a core technical challenge, as excessive latency disrupts conversational flow and undermines user trust.

Traditional transformer-based AI models process sequences as whole units, which inherently increases latency—posing barriers for real-time voice applications demanding immediate response. Cartesia addresses this with State Space Models, which treat sequences as continuously evolving systems, processing input stepwise and maintaining a compact internal state that supports streaming with minimal delay.

SSMs, a novel AI architecture for audio tasks, offer several advantages over transformers: they capture long-range dependencies without signal degradation, maintain a constant memory footprint regardless of sequence length, and natively support streaming inference. These features collectively enable production-grade voice AI models optimized for ultra-low latency.

Cartesia’s flagship models, Sonic (text-to-speech) and Ink (speech-to-text), demonstrate sub-100ms first-byte latency coupled with studio-quality output across 44 languages. Sonic supports seamless voice cloning from small datasets and context-aware synthesis, while Ink delivers real-time transcription with robust turn detection, noise resilience, and multi-speaker management.

Deployment options on AWS provide flexibility; Sonic is available as an Amazon SageMaker AI model package for quick in-VPC deployment, ideal for teams looking for managed scaling and minimal operational overhead. Alternatively, organizations with specific requirements can self-manage Sonic on EC2 GPU instances, controlling runtime, scaling, and networking but assuming additional operational responsibilities.

Cartesia’s voice AI models are production-proven within Amazon Connect, a cloud-based contact center service handling millions of interactions. Integration yields instantly responsive, natural conversational experiences supporting multilingual customers and scalable handling of peak call volumes.

Despite its advantages, SSM-based voice AI is a complex technology requiring GPU resources and careful infrastructure tuning to achieve latency targets under 100ms. Certain deployment scenarios may demand dedicated operational expertise for managing scaling, monitoring, and compliance needs.

By combining SSM architectures with AWS’s robust cloud infrastructure, Cartesia sets a new standard for real-time voice AI, enabling applications in contact centers, telehealth, gaming, and accessibility that require simultaneously low latency and high speech quality.