LLM Infra (2026)

LLM infrastructure tools sit between models and applications: clients that unify access to many providers, runtimes for local models, sandboxes and benchmarks for computer-use agents, and frameworks for training and reinforcement learning. They matter when you need control over where models run, how much they cost and how agents are evaluated.

Decide whether you need local inference (privacy, offline use, predictable cost) or unified access to hosted models. For agent infrastructure, check isolation guarantees and how evaluations are reproduced. For training and RL frameworks, look at supported model sizes, hardware requirements and documentation, since setup effort varies widely.