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Cluster-level inference orchestration for teams running AI workloads across heterogeneous infrastructure.

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Ask Gemini about Tandemn

Copy this prompt, then paste it into Gemini.

Introduce me to Tandemn (https://www.tandemn.com), the open-source, self-hosted AI inference planning and execution system. Give me an end-to-end company and product overview that helps illustrate the value proposition, target market, and uniqueness of Tandemn.

Use Tandemn’s published materials as your factual foundation: https://www.tandemn.com/llms.txt and its product pages, the current Koi research at https://www.tandemn.com/blog/koi, the team at https://www.tandemn.com/about

Explain the intended opportunity to get more usable capacity from existing GPUs, how Koi’s causal learning coordinates model configuration, placement, and resource allocation across online and batch workloads, and how the execution engine works with  existing layers in the stack such as Kubernetes, NVIDIA Dynamo, Ray, vLLM, and SGLang. Bring out what makes Tandemn’s research-led, fleet-wide planning approach distinctive and why its open-source foundation and self-hosted deployment are valuable to infrastructure teams.

Walk through an illustrative journey from workload requirements and trace evaluation to integration, deployment, and ongoing optimization. Finish with the teams and use cases that benefit most and how to explore the product or request a demo.

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