vLLM vs Ollama in production: the 2026 benchmark (single user, batching, multi-user)
Real benchmark of the two inference runtimes on RTX 5090 and 2x RTX 5090 NVLink. Single user, 4 concurrent users, 10 users under load: who wins when, and why continuous batching changes everything.
You have your AI rig, your quantized model, but what serves the inference? In 2026, two runtimes dominate: Ollama for simplicity and vLLM for production. We benched both on the same machines — here are the numbers and the final recommendation.
The 1-paragraph verdict
Ollama if you are alone or 2-3 people and want to install/test a model in 5 minutes. vLLM if you serve more than 3 concurrent users, every token matters, and you can invest 2-3h of setup. No match — they answer two different problems.
Single user, short prompt
| Llama 3.3 70B Q4 · 2× RTX 5090 | Ollama 28 tok/s · vLLM 32 tok/s | vLLM +14% |
| Qwen 3 30B MoE · 1× RTX 5090 | Ollama 44 tok/s · vLLM 48 tok/s | vLLM +9% |
| Llama 3.3 70B Q4 · 1× RTX 5090 (offload) | Ollama 9 tok/s · vLLM 11 tok/s | vLLM +22% |
4 concurrent users — the moment of truth
| Llama 3.3 70B Q4 · 2× RTX 5090 | Ollama 30 tok/s cumulative | vLLM 98 tok/s cumulative · ×3.3 |
| Qwen 3 30B MoE · 1× RTX 5090 | Ollama 46 tok/s cumulative | vLLM 156 tok/s cumulative · ×3.4 |
10 concurrent users — production case
| Llama 3.3 70B Q4 · 2× RTX 5090 | Ollama 47s P95 latency | vLLM 8s P95 · ×6 faster |
| Qwen 3 30B MoE · 1× RTX 5090 | Ollama 32s P95 latency | vLLM 5s P95 · ×6 faster |
How to choose for your LocalIA rig
- Starter (1× RTX 5090): Ollama for solo dev simplicity.
- Pro (2× RTX 5090): vLLM for team batching — non-negotiable.
- Enterprise (2× A6000 NVLink): vLLM mandatory for throughput.
Recommended hybrid setup on LocalIA Pro and Enterprise rigs: install both. Ollama for dev/debug, vLLM for production serving. They share the same HuggingFace model cache, so no double download.
Open the calculator / ask us for advice with your target model, users and constraints.