Written by Jakub Rusinowski · Last updated September 19, 2026
Trained from scratch on the MareNostrum 5 supercomputer at the Barcelona Supercomputing Center, covering all 24 official EU languages. The European answer to models whose multilingual coverage is an afterthought: EU languages are the training target, not a side effect.
| Licence | What it permits | Applies to |
|---|---|---|
Apache-2.0 | Commercial use permitted Commercial use permitted. No usage restrictions beyond attribution. | EuroLLM 9B, EuroLLM 22B |
| EuroLLM 9B | Min 6 GB VRAM · Q4_K_M · 4,096 ctx · |
| EuroLLM 22B | Min 14 GB VRAM · Q4_K_M · 4,096 ctx · |
The cheapest GPU that runs EuroLLM locally (min 6 GB VRAM) is the Intel Arc B570 (10 GB).
Install Ollama then run: ollama run eurollm
Minimum VRAM: 6 GB. For best results use Q4_K_M quantization.
EuroLLM needs about 6 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: EuroLLM 9B (6 GB, Q4_K_M); EuroLLM 22B (14 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — EuroLLM runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
Q4_K_M is the best balance of quality and VRAM for EuroLLM in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
EuroLLM has no local Ollama tag — the published tag is cloud-hosted, so running it sends your prompts to a hosted GPU rather than your own machine.