From Ollama to vLLM: a roadmap for scaling LLM deployment
kalyan_kpl · x · 2026-09-15
A practical roadmap for scaling LLM inference: when to start with Ollama and when to move to vLLM.
- Ollama: open-source tool for running LLMs locally on MacBook/Windows/Linux; best for prototyping, air-gapped privacy-sensitive apps (government/healthcare/legal), low-volume workloads, and CPU/low-end GPU hardware.
- vLLM: high-throughput inference library from UC Berkeley's Sky Computing Lab, optimized for NVIDIA GPUs; fits high-traffic services, multi-GPU deployments of large models like DeepSeek-Coder-V2 (236B params), and low-latency production environments.
- Analogy: Ollama is a bicycle—easy but not for highways; vLLM is a sports car—fast but demanding infrastructure and expertise.
Bottom line: prototype with Ollama, migrate to vLLM for scalable production.
Related event: Roadmap for Scaling LLM Deployment from Ollama to vLLM(2 posts)→
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