Choosing Between Ollama and vLLM

Two Popular Choices, Two Different Jobs

Ollama and vLLM are both open-source tools for running large language models on your own infrastructure, and it's common to see them compared as if they were interchangeable. In practice, they solve different problems. Ollama is built to make running a model as simple as a single command. vLLM is built to serve a model to hundreds of concurrent users at the lowest possible latency and cost per token. Picking the wrong one for your use case means either fighting unnecessary complexity or hitting a throughput wall much sooner than expected.

Ollama: Built for Simplicity

Ollama packages model weights, a runtime, and a simple REST API behind a single binary. Pulling and running a model is a one-line command, and switching between models — Llama, Mistral, Gemma, DeepSeek — is just as easy. This makes Ollama the natural choice for internal tools, prototypes, and single-tenant deployments like a private ChatGPT assistant, where the priority is getting a working assistant in front of a small group of users quickly, not squeezing out maximum GPU throughput.

The trade-off is concurrency. Ollama processes requests with a simpler scheduling model than vLLM, so as concurrent users grow into the dozens or hundreds, latency degrades faster than with a purpose-built serving engine.

vLLM: Built for Throughput

vLLM's core innovation, PagedAttention, manages the KV cache as virtual memory pages, which eliminates fragmentation and enables continuous batching — new requests are inserted into an in-flight batch as capacity frees up, rather than waiting for a fixed batch window. On typical benchmarks, this gives vLLM 10–20x higher throughput than naive model-serving approaches at equivalent latency targets, with an OpenAI-compatible API that drops into existing integrations without code changes.

That performance comes with more operational surface area: model configuration, tensor parallelism settings, and GPU memory tuning all matter more than they do with Ollama. For a production API serving many concurrent clients, that complexity is worth it.

Head-to-Head

• Concurrency: Ollama comfortably handles 10–50 concurrent users; vLLM is built for hundreds of concurrent requests
• Setup complexity: Ollama is near-zero configuration; vLLM has more tuning knobs
• API compatibility: Both expose OpenAI-compatible endpoints
• Best fit: Ollama for internal assistants and knowledge bases; vLLM for production APIs and multi-tenant workloads

Recommended NexNodo Deployment

For an Ollama-based internal assistant, a Cloud GPU VPS Small (1× H200 GPU, 15 vCPU, 256 GB RAM, 1 TB Storage — $3.79/hr or $2,767/mo) is more than sufficient. For a vLLM-based production API, the recommended deployment is a Managed Kubernetes GPU Medium (2× H200 GPU, 30 vCPU, 512 GB RAM, 2 TB Storage — $7.90/hr per node or $5,767/mo), giving enough headroom for concurrent request handling and future model additions.

Deploy Either in Minutes

Start with the Private ChatGPT Template for an Ollama-based assistant, or the Private AI API Platform Template for a production vLLM deployment — both provision infrastructure and applications together in a single deployment.