Koi: Evidence-Guided, Self-Calibrating Planning for Multi-Cloud, Heterogeneous LLM Inference Fleets

Deploying a large language model (LLM) requires choosing the right accelerator, parallelization strategy, engine configuration, and replica count amongst 140 other variables across clouds, regions, and markets. These decisions are intertwined, and the best cluster-wide plan continually changes as jobs contend for shared capacity on shifting workloads. We present Koi, a self-calibrating cluster planner for heterogeneous, multi-cloud fleets. Koi uses a Recursive Language Model (RLM) hierarchy to propose hypotheses and reason over deployment while deterministic components predict serving behavior, score candidates, and control placement. It represents inference behavior as a fixed causal graph linking decisions (e.g., accelerator, parallelism), runtime mediators (e.g., KV-cache pressure, communication overhead), and outcomes (e.g., latency, throughput). Each deployment strengthens or weakens scoped causal relationships, building a comprehensive playbook for inference that the planner employs to guide future decisions. Job-wise configurations are composed into cluster-wide plans and evaluated against serving objectives, resource constraints, SLO risks, and cost. Across benchmarks, Koi achieves 1.18x greater goodput than the strongest baseline while reducing /token by 20% in a real heterogeneous cluster. Ablations isolate the contribution of Koi's architecture: a pure language model attains a composite score of 35.3, the same model equipped with Koi's deterministic tools reaches 51.6, and the full Koi planner reaches 70.8.