{"slug":"elevenlabs-scaling-voice-ai-with-gpu-accelerated-infrastructure","url":"https://findausecase.com/use-cases/elevenlabs-scaling-voice-ai-with-gpu-accelerated-infrastructure","title":"ElevenLabs: Scaling Voice AI with GPU-Accelerated Infrastructure","description":"ElevenLabs runs its voice AI platform for text-to-speech, voice cloning and speech-to-speech translation on Google Kubernetes Engine (GKE) with NVIDIA GPUs, using GPU optimization strategies such as Multi-Instance GPUs and GPU time sharing to improve utilization and reduce costs. The platform generates 600 hours of audio for every hour of real time, supports 29 languages (expanding to 40), and is reportedly used by 41% of Fortune 500 companies, powering products including AI Dubbing Studio, long-form audio for audiobooks and podcasts, article narration for outlets like The New York Times, and real-time conversational AI assistants.","company":"ElevenLabs","industry":"Media & Entertainment","aiCapabilities":["Speech & Audio AI","Generative AI","Conversational AI"],"technology":["Google Kubernetes Engine (GKE)","NVIDIA H100 GPUs","Multi-Instance GPUs (MIG)","GPU Time Sharing"],"deployment":"Public Cloud","problemStatement":"As one of the fastest-growing AI companies, ElevenLabs needed to run massive-scale voice AI inference — producing 600 hours of generated audio for every hour of real time — while improving GPU utilization and controlling infrastructure costs.","solutionApproach":"ElevenLabs built its voice AI platform for text-to-speech, voice cloning and speech-to-speech translation on Google Kubernetes Engine (GKE) with NVIDIA GPUs, including H100s. It used GPU optimization strategies such as Multi-Instance GPUs (dividing a single GPU into seven hardware-isolated slices) and GPU Time Sharing (context-switching between processes on a GPU) to improve utilization and reduce costs. Its foundational model development followed an iterative approach: starting with a text-to-speech model, extending to multiple languages, adding voice cloning, and then building a speech-to-speech model for real-time translation.","businessValue":"The platform generates 600 hours of audio for every hour of real time, supports 29 languages (expanding to 40), and is reportedly used by 41% of Fortune 500 companies. It powers products including AI Dubbing Studio, long-form audio for audiobooks and podcasts, article narration for outlets like The New York Times, and real-time conversational AI assistants.","evidence":{"band":"high"},"sourceUrl":"https://www.zenml.io/llmops-database/scaling-voice-ai-with-gpu-accelerated-infrastructure","dates":{"publishedAt":"2026-08-16T13:32:54.925Z","publishedAtSource":"ledger","updatedAt":"2026-08-26T06:58:33.164Z"},"license":"Open for reading and citing with a link to https://findausecase.com/use-cases/elevenlabs-scaling-voice-ai-with-gpu-accelerated-infrastructure. Bulk republication requires permission."}