vLLM vs Ollama: Which to Use for Running LLMs (2026)

Different jobs, same models

Ollama and vLLM both run open models, but they're built for different situations. Ollama is designed for one machine and one user at a time, with a one-command install, models pulled by name, and a local API. vLLM is a serving engine for GPU servers, built to handle many concurrent requests from the same hardware. Most of the decision comes down to how many people the model needs to serve.

When Ollama is the better fit

Ollama installs on macOS, Windows, and Linux, runs on laptops and workstations, and keeps a local REST API running with official Python and JavaScript libraries. It suits local development, private single-user workloads, and powering coding agents, and it adds cloud models for when local hardware isn't enough. What it isn't built for is high-concurrency serving, and its cloud plans cap concurrent requests by tier.

When vLLM is the better fit

vLLM manages GPU memory with PagedAttention and batches incoming requests continuously, which lets one set of GPUs serve many users at once. It supports tensor, pipeline, and expert parallelism for models too large for a single GPU, along with quantization, speculative decoding, and multi-LoRA serving. Its server exposes an OpenAI-compatible API, plus the Anthropic Messages API, and runs on NVIDIA, AMD, and Intel GPUs and on TPUs. The tradeoff is operational: you run, scale, and monitor the GPU servers yourself, and setup is heavier than a desktop install.

A common path between them

Many teams prototype on Ollama and move to vLLM once real traffic arrives, since both serve the same open models through OpenAI-style APIs. Respan covers both sides of that move. It traces Ollama calls through a native integration and can register a vLLM server as a custom model, so tracing, cost tracking, and evals carry over when the serving layer changes.