Guide to Deploying Low-Latency Real-Time Voice AI on Dedicated GPU Hardware

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A tutorial explains that cloud platforms often fail to meet the roughly 500-millisecond response budget needed for natural voice AI conversations due to shared hypervisors and network overhead. It argues that bare-metal, dedicated GPU infrastructure is required to run the full pipeline of voice activity detection, speech-to-text, language model, and text-to-speech together in GPU memory. Recommended hardware ranges from an RTX 4090 for proof-of-concept builds handling 1-2 calls, to RTX A6000, L40, or L40S cards for small business deployments, to H100 GPUs for enterprise-scale concurrency with large models. The guide then walks through setting up Ubuntu 22.04 with NVIDIA drivers, CUDA, Docker, and the NVIDIA Container Toolkit. It also covers deploying the LiveKit media server to stream audio with minimal buffering.

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