Teuken 7B Instruct
By OpenGPT-X · Germany
Updated 2026-07-13
Overview
A German government-funded (BMWK) 7B from OpenGPT-X covering all 24 EU languages. The commercial variant is Apache 2.0; the research-v0.4 variant has restricted terms.
When to pick this model
- You need EU sovereignty credentials with German institutional backing
- You're building products across all 24 EU languages
- You need an Apache-licensed European 7B for commercial deployment
- You're working on public-sector or DACH-region projects
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 5 GB |
| Q5_K_M | 6 GB |
| Q8_0 | 9 GB |
| FP16 (no quantization) | 14 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, Teuken 7B Instruct fits an 8 GB consumer card at Q4_K_M (5 GB). Stepping up to Q8_0 nearly doubles the footprint to 9 GB, and unquantized FP16 weights take 14 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Teuken 7B Instruct needs roughly 10 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 12 tokens/sec on entry-level GPUs, on the order of 35 tokens/sec on a mid-range card, and up to 90 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Teuken 7B 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 Teuken 7B Instruct |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (6 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (9 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (14 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (14 GB used) |
| 32 GB | RTX 5090 | FP16 (14 GB used) |
Which GPU should you buy to run Teuken 7B Instruct?
To run Teuken 7B Instruct locally at Q4, you need ~5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Built for EU sovereignty
- Covers all 24 official EU languages
- Apache 2.0 commercial variant available
- German government (BMWK) backing
Limitations
- Only 4K context
- No official Ollama tag
- Research-v0.4 variant carries a restricted license — check which you grabbed
Typical workloads
In our catalog grid, Teuken 7B Instruct is filed under EU Sovereignty, 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 (commercial) license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense · 32 layers · GQA (2 groups) · SwiGLU · RoPE · multilingual tokenizer
Training: 24 EU languages corpus, funded by BMWK (Germany).
An EU sovereignty pick with the commercial variant clearly licensed — confirm which variant you're using.
Quick start
# HuggingFace : openGPT-X/Teuken-7B-instruct-commercial-v0.4Or 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 Teuken 7B Instruct need?
At the recommended Q4_K_M quantization, Teuken 7B Instruct needs about 5 GB of VRAM. Q8_0 takes 9 GB, and unquantized FP16 weights take 14 GB.
Can Teuken 7B Instruct run without a GPU?
Yes — with roughly 10 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 Teuken 7B Instruct support?
Teuken 7B Instruct supports a 4k-token context window (4,096 tokens).
Can I use Teuken 7B Instruct commercially?
Yes. Teuken 7B Instruct is released under Apache 2.0 (commercial), a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Teuken 7B Instruct on consumer hardware?
Our compatibility engine estimates on the order of 35 tokens/sec on a mid-range GPU and up to 90 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Teuken 7B Instruct should I download first?
Start with Q4_K_M (5 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.