Cerebrium

Cerebrium provides a serverless GPU infrastructure platform designed specifically for real-time AI workloads, including voice agents, video models, large language models (LLMs), and other computationally intensive AI applications. The core value proposition lies in its ability to launch containers in seconds with sub-second cold starts, achieved through memory and GPU snapshotting technology. This allows users to serve AI models with low latency from the very first request, avoiding the long initialization times typically associated with traditional GPU deployments. The platform supports automatic scaling that reacts instantly to traffic bursts, eliminating the need for manual capacity planning or Kubernetes management. Users can bring their own code and models, deploying them as serverless functions without worrying about underlying cluster provisioning, node selection, or autoscaling configurations. Cerebrium handles GPU scaling elastically, spinning up instances on demand and shutting them down when idle, which reduces costs by charging only for the compute seconds actually consumed. Key technical features include built-in observability and monitoring tools that track request latency, error rates, GPU utilization, and memory usage. The platform integrates with popular model frameworks such as vLLM for LLM serving, Qwen for language models, and Stable Diffusion XL for image generation, providing optimized inference runtimes out of the box. Users can also define custom inference endpoints with configurable timeouts, concurrency limits, and GPU types (e.g., A100, H100, L4). For real-time voice applications, Cerebrium supports streaming audio processing with low-latency responses, enabling use cases like voice assistants, real-time transcription, and conversational AI. V