Running Local LLMs with Ollama: 3 Levels from Laptop to Cluster-Scale Distributed Inference

Running a local large language model (LLM) with Ollama provides an accessible and private way for individuals to experiment with AI models, ideal for personal use and prototyping. However, as the need for scalability and performance grows, users often progress through three levels of LLM deployment: starting with local setups, moving to high-performance server-grade runtimes like vLLM, and eventually adopting full-scale distributed inference systems such as the Bento Inference Platform. Each level addresses increasing demands in terms of concurrency, latency, and operational complexity, with Ollama being suited for initial experiments, high-performance runtimes offering server-grade performance, and distributed systems providing scalable, efficient, and resilient infrastructures for enterprise-level tasks. The Bento Inference Platform simplifies the management of distributed systems, offering features like cross-region deployment, autoscaling, and enhanced security, ultimately allowing teams to focus on product development instead of infrastructure challenges.

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