Cornserve: Easy, Fast, and Scalable Multimodal AI

vLLM : Cornserve = Monolith : Microservice

Multimodal AI models like Qwen 3 Omni are becoming increasingly complex and heterogeneous.

Cornserve is a distributed serving system for multimodal AI. Cornserve performs model fission and automatic sharing of common components (e.g., LLMs, Vision Encoders, Audio Generators) across applications on your infrastructure.

  1. Independent scaling: Each component of complex multimodal models (e.g., LLMs, vision encoders, audio generators) can be scaled independently based on incoming request load.
  2. Less interference: For instance, some Vision-Language Model requests may have three images, while some may have none. When all is crammed into a single monolithic server, multimodal embedding and LLM text generation can interfere with and delay each other. Model fission allows each component to run in isolation, reducing interference and improving latency.
  3. Lower complexity: A single monolithic server that handles multimodal inputs, LLM text generation, and multimodal outputs is extremely complex to build and maintain. Cornserve is the substrate that allows the composition of simpler task executors (microservices) into complex multimodal AI applications.

  • Model fission

    Split up your complex models into smaller components and scale them independently.

  • Automatic sharing

    Common model components are automatically shared across applications.

  • Multimodal-native

    Cornserve is built multimodal-native from the ground up. Image, video, audio, and text are all first-class citizens.

  • Simple K8s deployment

    One-command deployment to Kubernetes with Kustomize.

  • Observability

    Built-in support for OpenTelemetry to monitor your apps and requests.

  • Open Source, Apache-2.0

    Cornserve is open-source with the Apache 2.0 license and is available on GitHub.