EuroLLM 22B Instruct 2512

Overview

Utter Project's 22.6B EU-sovereign model released February 2026 covering 35 European languages with 32k context — the heavy-duty successor to EuroLLM 9B.

When to pick this model

• EU-sovereign workloads needing more than EuroLLM 9B can deliver
• Multilingual production chat across 35 European languages
• Long-context European-language workflows up to 32k
• Public-sector deployments requiring open weights and EU provenance
• Migration target for teams running the 9B variant in production

VRAM requirements by quantization

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 22B Instruct 2512 needs a 16 GB card at Q4_K_M (13 GB). Stepping up to Q8_0 nearly doubles the footprint to 24 GB, and unquantized FP16 weights take 45 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, EuroLLM 22B Instruct 2512 needs roughly 24 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 4 tokens/sec on entry-level GPUs, on the order of 16 tokens/sec on a mid-range card, and up to 40 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches EuroLLM 22B Instruct 2512 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.

Which GPU should you buy to run EuroLLM 22B Instruct 2512?

To run EuroLLM 22B Instruct 2512 locally at Q4, you need ~13 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).

Strengths

• 22B scale gives meaningful headroom over EuroLLM 9B
• Apache 2.0 license
• 32k context handles document-length European-language workloads
• EU sovereignty across the full project stack
• Fresh February 2026 release with current training data

Limitations

• No official Ollama distribution at launch
• Smaller community than mainline open models
• Tooling and quantization support still maturing

Typical workloads

In our catalog grid, EuroLLM 22B Instruct 2512 is filed under EU Sovereignty 22B, Multilingual — 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.

The 32k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: Dense 22.6B · 56 layers · GQA 48Q/8KV · SwiGLU · RoPE θ=1M

Training: 35 EU + relevant languages.

Quick start

# HuggingFace : utter-project/EuroLLM-22B-Instruct-2512

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 22B Instruct 2512 need?

At the recommended Q4_K_M quantization, EuroLLM 22B Instruct 2512 needs about 13 GB of VRAM. Q8_0 takes 24 GB, and unquantized FP16 weights take 45 GB.

Can EuroLLM 22B Instruct 2512 run without a GPU?

Yes — with roughly 24 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 22B Instruct 2512 support?

EuroLLM 22B Instruct 2512 supports a 32k-token context window (32,768 tokens).

Can I use EuroLLM 22B Instruct 2512 commercially?

Yes. EuroLLM 22B Instruct 2512 is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is EuroLLM 22B Instruct 2512 on consumer hardware?

Our compatibility engine estimates on the order of 16 tokens/sec on a mid-range GPU and up to 40 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of EuroLLM 22B Instruct 2512 should I download first?

Start with Q4_K_M (13 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q8_0.