vLLM or Ollama for a local LLM server?
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Use vLLM when several people share a local LLM server, because it batches requests and keeps per-user speed high. Use Ollama for one user. At 8 concurrent requests in Olares' own test, vLLM gave about 81 tokens/s each and Ollama 32.
Olares One → Local LLM server → vLLM · Ollama · OpenAI-compatible API → Your models, served on your network
vLLM or Ollama for a local LLM server: which should you run?
vLLM or Ollama for a local LLM server comes down to concurrency. Run vLLM when several people use the server at the same moment, and Ollama when one person uses it at a time. Both load open models, both run on NVIDIA GPUs and both speak an OpenAI-compatible API. They're built for different jobs.
Ollama wraps llama.cpp. It pulls GGUF models with one command, swaps them in and out of memory on demand, and runs on Windows, macOS and Linux. vLLM is a throughput engine. Continuous batching adds new requests to the running batch at every step, and PagedAttention stores the KV cache in small pages so memory isn't wasted between requests.
How far apart are they on the same hardware?
On the same hardware the two engines are close with one user and far apart with eight. In Olares' own test on Olares One with Qwen3-30B-A3B, vLLM ran at 157 tokens per second for one request and about 81 per request at 8 concurrent. Ollama ran at 107 for one request and 32 per request at 8.
| Engine (Olares' own test) | 1 request | 8 concurrent, per request |
|---|---|---|
| vLLM | 157 tokens/s | about 81 tokens/s |
| Ollama | 107 tokens/s | 32 tokens/s |
When does Ollama still make sense?
Ollama still makes sense when convenience matters more than throughput. Setup takes minutes and the model library is easy to browse. It unloads idle models, so one GPU can offer many models to occasional users. GGUF builds exist for almost every open model, and it can spill part of a model into system RAM when VRAM runs out.
vLLM is built to keep the model on the GPU, so a model that doesn't fit needs a smaller quantization or a bigger GPU.
Can you run both on one server?
Yes, and many teams do. vLLM serves the one model everyone uses all day, while Ollama holds the others for occasional tests. They compete for the same VRAM, so cap vLLM's memory share and leave room for whatever Ollama loads. On Olares One, Ollama is a one-click install from the Olares Market.
Olares' cluster feature also uses vLLM, splitting a model across devices with pipeline parallelism. For sizing, read a server for multiple users. For connecting apps, read an OpenAI-compatible API.
Questions & answers
Is vLLM harder to set up than Ollama?
Somewhat. You pick a model format the GPU engine supports, set memory and context limits, and usually run it in a container. Ollama hides most of those choices.
Does vLLM run on a Mac?
vLLM is built for NVIDIA GPUs first. On a Mac, llama.cpp, Ollama and MLX are the usual engines.
Sources for this entry
- Olares blog: local AI hardware benchmarking (Oct 2025) (Company-published)
- Olares One FAQ (Olares docs) (Company-published)
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Local LLM Server. “vLLM or Ollama for a local LLM server?.” Content reviewed 13 November 2026.
https://localllmserver.com/vllm-vs-ollama/