EuroLLM 9B Instruct
By Utter Project / UE · European Union
Updated 2026-07-13
Overview
An EU-funded 9B (Horizon Europe) covering 35 languages including all 24 official EU ones. Trained on 4T tokens on the MareNostrum5 supercomputer and released under Apache 2.0.
When to pick this model
- You're building EU-focused products that need sovereignty optics
- You need broad coverage of all 24 official EU languages
- You want an Apache 2.0 model from a European institution
- You're working on policy or public-sector projects with EU procurement
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 6 GB |
| Q5_K_M | 7 GB |
| Q8_0 | 10 GB |
| FP16 (no quantization) | 18 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, EuroLLM 9B Instruct fits an 8 GB consumer card at Q4_K_M (6 GB). Stepping up to Q8_0 raises the footprint to 10 GB, and unquantized FP16 weights take 18 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, EuroLLM 9B Instruct needs roughly 12 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 10 tokens/sec on entry-level GPUs, on the order of 30 tokens/sec on a mid-range card, and up to 80 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches EuroLLM 9B Instruct to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for EuroLLM 9B Instruct |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (7 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (10 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (10 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (18 GB used) |
| 32 GB | RTX 5090 | FP16 (18 GB used) |
Which GPU should you buy to run EuroLLM 9B Instruct?
To run EuroLLM 9B Instruct locally at Q4, you need ~6 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- EU sovereignty — Horizon Europe funded
- Apache 2.0 license
- Best European open model at its scale
- Strong coverage of all 24 official EU languages
Limitations
- Only 4K context — short for modern workloads
- No official Ollama tag
- Outpaced by Qwen 3 8B on general benchmarks
Typical workloads
In our catalog grid, EuroLLM 9B Instruct is filed under EU Sovereignty, 24 European Languages — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads; French-language output where quality matters.
Note the 4k-token context window — fine for short interactions, limiting for long documents or big retrieval contexts. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense · 42 layers · GQA (32Q/8KV) · SwiGLU · RoPE · RMSNorm
Training: 4T tokens on MareNostrum5 (BSC). 35 languages (24 EU + extras).
The natural pick when EU sovereignty or procurement requires a European-trained Apache 2.0 model.
Quick start
# HuggingFace : utter-project/EuroLLM-9B-Instruct (pas d'Ollama officiel)Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does EuroLLM 9B Instruct need?
At the recommended Q4_K_M quantization, EuroLLM 9B Instruct needs about 6 GB of VRAM. Q8_0 takes 10 GB, and unquantized FP16 weights take 18 GB.
Can EuroLLM 9B Instruct run without a GPU?
Yes — with roughly 12 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does EuroLLM 9B Instruct support?
EuroLLM 9B Instruct supports a 4k-token context window (4,096 tokens).
Can I use EuroLLM 9B Instruct commercially?
Yes. EuroLLM 9B Instruct is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is EuroLLM 9B Instruct on consumer hardware?
Our compatibility engine estimates on the order of 30 tokens/sec on a mid-range GPU and up to 80 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of EuroLLM 9B Instruct should I download first?
Start with Q4_K_M (6 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.