AI-powered SDKs and microservices for high-quality, real-time video and audio communication.
NVIDIA Maxine is a high-performance suite of GPU-accelerated SDKs and cloud-native microservices designed to revolutionize real-time communication. Built on the NVIDIA RTX platform, Maxine leverages state-of-the-art AI models to address common video conferencing challenges such as poor lighting, background noise, and lack of eye contact. As of 2026, the platform has transitioned heavily toward NVIDIA NIM (NVIDIA Inference Microservices), allowing for seamless deployment across hybrid cloud environments and edge devices. Technically, it utilizes Tensor Cores to execute complex deep learning models for video upscaling, noise cancellation, and facial expression estimation with sub-millisecond latency. Its market position is dominant within the enterprise sector, powering major integrations for Zoom, Microsoft Teams, and Logitech. The architecture is optimized for high-density deployments, allowing service providers to support thousands of concurrent AI-enhanced streams per server. By offloading heavy compute tasks to the GPU, Maxine enables features like 'Live Portrait' and 'Eye Contact' that were previously impossible in real-time consumer software without specialized hardware.
Uses facial landmark tracking and neural texture synthesis to realign eyes toward the camera lens.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Upsamples narrowband audio (8kHz) to wideband (16kHz or 48kHz) using generative AI.
Deep learning-based upscaling that reconstructs high-frequency details from low-resolution source video.
Drives a single 2D headshot image using the facial movements of a live speaker.
Deep learning model that isolates and removes acoustic reflections from suboptimal recording environments.
Extracts 3D facial blendshapes and expression weights for driving digital avatars.
State-of-the-art denoising that filters out non-speech sounds like typing, fans, or pets.
Employees in distracting home environments with poor lighting and background noise.
Registry Updated:2/7/2026
Instructors reading from notes appear disengaged because they aren't looking at the camera.
Streaming high-quality video over unstable cellular networks.