"Text was the first frontier. Vision and audio are next. Real-time multimodal AI is the ultimate test of fast infrastructure."
Text-based LLMs have dominated the AI conversation, but the future is multimodal. Vision models that see, audio models that hear, and systems that combine all modalities in real-time. These applications demand even stricter latency requirements than text alone.
Multimodal models are computationally expensive. Processing a single image can take 50-100ms. Audio streams require continuous inference. Video combines both. Meeting sub-100ms latency with multimodal inputs is orders of magnitude harder than text.
Traditional approaches process images sequentially: upload, encode, then generate. This adds latency at every step. Real-time vision requires parallel processing—encoding and generating simultaneously, streaming results as they become available.
Upload → Encode → Generate → Return (1.2s total)
Encode + Generate in parallel → Stream (200ms total)
Audio is inherently streaming. Speech arrives continuously, and users expect real-time transcription and response. True conversational AI requires processing audio chunks in under 20ms to enable natural back-and-forth dialogue.
Our API supports vision and multimodal models with the same blazing fast speed. Send images, receive instant analysis. The same speed standard, regardless of modality.
Real-time multimodal enables applications that were previously impossible: live video analysis for accessibility, instant visual search, conversational AI with spatial awareness, and ambient computing experiences that see, hear, and respond naturally.
The multimodal frontier is the true test of fast infrastructure. Text was the warm-up. Vision and audio are the main event.