vLLM vs Ollama en produccion: el benchmark 2026 (single user, batching, multi-user)
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Damien · LocalIABenchmark real de los dos runtimes de inferencia en RTX 5090 y 2x RTX 5090 NVLink. Single user, 4 usuarios simultaneos, 10 usuarios bajo carga: quien gana cuando y por que el continuous batching lo cambia todo.
Articulo traducido. Esta version esta localizada para evitar mezclar interfaces internacionales con texto frances. Los datos tecnicos, importes y recomendaciones se mantienen iguales.
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.
Abre la calculadora / escríbenos para un consejo con tu modelo objetivo, usuarios y restricciones.
Preguntas frecuentes
vLLM or Ollama to serve an LLM in production?+
vLLM wins from 3+ concurrent users. Continuous batching gives ~3.3x total throughput and ~6x better P95 latency at 10 users versus Ollama. Ollama wins on install simplicity and for development / 1-2 users.
What real gain from vLLM vs Ollama on Llama 70B?+
On 2x RTX 5090: single user vLLM +14% (32 vs 28 tok/s). 4 concurrent users: vLLM 3.3x (98 vs 30 tok/s combined). 10 users: vLLM P95 6x faster (8s vs 47s).
Does vLLM use more VRAM than Ollama?+
No, vLLM uses ~6-8% less VRAM thanks to PagedAttention (paged KV-cache management, less fragmentation). On Llama 70B Q4: vLLM 44 GB, Ollama 47 GB. The gap widens on large contexts.
When to prefer Ollama over vLLM in 2026?+
For solo development / R&D, for Apple Silicon (vLLM does not support MPS), for frequent model hot-swapping, for 1-2 person usage. For multi-user production, vLLM is mandatory.
Can you use both in parallel?+
Yes, it is the recommended setup on a Pro/Enterprise build: Ollama for fast dev/debug, vLLM for production serving. Both share the same HuggingFace cache, so no double download.
vLLMOllamaProduccion