vLLM vs Ollama: Key differences, performance, and how to run them

Large language models have evolved beyond research tools to power various applications, yet deploying them efficiently remains complex due to factors like latency, memory, and cost. Two open-source projects, vLLM and Ollama, offer distinct solutions: vLLM focuses on high-performance inference using PagedAttention and optimized GPU scheduling for handling production workloads with low latency, while Ollama emphasizes ease of use, allowing developers to run models locally with minimal setup, ideal for prototyping and experimentation. Choosing between them depends on the specific needs of performance versus simplicity, with vLLM excelling in scaling and production efficiency and Ollama providing straightforward accessibility for individual developers. Northflank, a full-stack AI cloud platform, facilitates the deployment of both tools, supporting varied workloads and enabling seamless transitions as user requirements change.