In this paper, we present Que2Search, a deployed query and product understanding system for search. Que2Search leverages multi-task and multi-modal learning approaches to train query and product representations. We achieve over 5% absolute offline relevance improvement and over 4% online engagement gain over state-of-the-art Facebook product understanding system by combining the latest multilingual natural language understanding architectures like XLM and XLM-R with multi-modal fusion techniques. In this paper, we describe how we deploy XLM-based search query understanding model that runs <1.5ms @P99 on CPU at Facebook scale, which has been a significant challenge in the industry. We also describe what model optimizations worked (and what did not) based on numerous offline and online A/B experiments. We deploy Que2Search to Facebook Marketplace Search and share our deployment experience to production and tuning tricks to achieve higher efficiency in online A/B experiments. Que2Search has demonstrated gains in production applications and operates at Facebook scale.