Large Language Models (LLMs) demonstrate strong multilingual capabilities but are costly to deploy due to their size and computational demands. To mitigate this, compression techniques such as pruning and quantization are widely used. However, these methods face two key limitations: (1) they assume access to high-quality instruction or calibration data, which is often unavailable for low-resource languages; and (2) they aim to preserve multilingual generality, making them inefficient for language-specific applications. We introduce LangCompress, a language-aware compression framework that enhances existing compression methods for targeted deployment. LangCompress is method-agnostic and improves state-of-the-art pruning and quantization approaches. It features two core components: an iterative self-supervised pipeline for generating instruction data in the target language, and a vocabulary simplification strategy that reduces the LM head to focus on key tokens. Experiments on perplexity, translation, and summarization tasks show that LangCompress improves performance in the target language. The code and data are publicly available.