Family EuroLLM · 22.6B parameters

EuroLLM 22B Instruct 2512

EuroLLM 22B version (Feb 2026). 35 EU languages. 32k context. Outperforms EuroLLM 9B on demanding tasks.

Utter Project·License Apache 2.0·Context 32k tokens·Output February 2026·Fits within the 128 GB of the GIGABYTE AI TOP ATOM← Catalog

01What it can do

Strengths
  • EuroLLM 22B version
  • Apache 2.0
  • 32k ctx
  • EU sovereignty
Limitations to know
  • —No official Ollama
Architecture
Dense 22.6B · 56 layers · GQA 48Q/8KV · SwiGLU · RoPE θ=1M
Training
35 EU + relevant languages.
Ideal for
EU sovereignty 22BMultilingual

04Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$# HuggingFace : utter-project/EuroLLM-22B-Instruct-2512
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
13 GB
Q5_K_M
Good quality/size compromise
16 GB
Q8_0
Nearly indistinguishable from FP16
24 GB
FP16
Full precision — server use
45 GB
Fallback CPU · If you don't have a GPU, allow 24 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for EuroLLM 22B Instruct 2512?

To run EuroLLM 22B Instruct 2512 locally with Q4 quantization, you need about 13 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.

Current offer: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395)
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Why this choice? Our complete guide on GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) →

Affiliate links — possible commission at no extra cost to you; independent recommendations. As an Amazon Associate, BestLLMfor earns from qualifying purchases.

On the go: EuroLLM 22B Instruct 2512 also runs on a RTX laptop PC (16 GB of VRAM) →

This model in your private ChatGPT, without the cloud

Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.

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03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~4t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~16t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~40t/s
RTX 4090, M4 Max, Radeon 7900