From Static to Polymorphic Inference: A new era for production open-source inference systems

Together AI describes “Polymorphic Inference” as an adaptive approach to AI serving designed for rapidly changing open-source model releases, architectures, workload patterns, and hardware platforms, contrasting it with older “Static Inference” configurations optimized for relatively fixed models and traffic. The company argues that hand-tuned kernels and fixed inference-engine settings often fail to remain optimal across diverse real-world conditions, particularly for evolving workloads and architectures such as mixture-of-experts models. Its proposed response is Continuous Compilation, led by the Together Compiler, which uses AI agents and iterative performance testing to search for and apply workload-specific optimizations in production. Together AI says the compiler is already used on selected serverless endpoints and plans to offer customers self-service optimization for dedicated deployments. Its broader research agenda includes adaptive speculative decoding, dynamic routing across heterogeneous infrastructure through ThunderAgent, post-training and customization of open-source models, and workload-aware routing among models, all intended to improve performance and expand access to AI systems.

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