
Evaluating STT performance for voice AI. The open source benchmark tests transcription latency and semantic accuracy.
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Deliver real-time video and audio at the highest possible quality, with infrastructure that scales horizontally and geographically, with media servers in 10 geographic regions and 30 availability zones. This delivers a "first hop" network latency of 13ms or less for 5 billion people.
Build an experience for 1:1 meetings or for 100,000 active participants, chat, reactions, and data messaging—all at real-time latencies.
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Select music or voice modes for audio, or take low-level control and customize bitrates and audio processing.
Use multiple cameras and mics. Switch between camera views. Support multiple languages. Manage audio track subscriptions and volumes independently on each client.
Bring participants to the stage with no delay. Add co-hosts to sessions and seamlessly transition between keynotes and panel discussions.

Evaluating STT performance for voice AI. The open source benchmark tests transcription latency and semantic accuracy.

We trained an AI model on a device that fits in your hand. Here's how the NVIDIA DGX Spark™ stacked up against datacenter and consumer GPUs.

Comparing LLM performance for voice agents. The open source benchmark aiewf-eval tests latency, tool calling, instruction following, and knowledge grounding in long, multi-turn conversations.