Update README.md

README.md

@@ -95,6 +95,20 @@ Following pre-training, the model undergoes supervised post-training (instructio
 
 Demo: [faust.tabularis.ai](https://faust.tabularis.ai)
 
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+> [!TIP]
+> **Designed for local and cost-efficient deployment.**  
+> Faust-1 is deliberately sized and optimized to run on **consumer-grade hardware** and **does not require expensive data-center GPUs**.
+>
+> **Typical deployment examples:**
+> - **Laptop / Desktop (CPU or small GPU):**  
+>   Runs on modern CPUs or entry-level GPUs (e.g. Apple Silicon, RTX 3060/4060, RX 6600) using optimized runtimes such as GGUF, MLX, or ONNX.
+> - **Single-GPU workstation:**  
+>   Efficiently serves interactive workloads on a single consumer GPU with low VRAM requirements compared to larger multilingual models.
+> - **On-device / privacy-sensitive setups:**  
+>   Suitable for local assistants, offline document analysis, and private RAG pipelines where data must not leave the machine.
+>
+> This makes Faust-1 practical for **researchers, developers, and small teams** who want strong German language performance without cloud dependency or high inference costs.
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 ## Model summary