In the Semantic Web, the Resource Description Framework (RDF) has become the standard representation to describe Internet resources. RDF data is structured in triples comprising a subject, a predicate and an object: the predicate defines the relation between subject and object. The RDF Schema (RDFS) extends raw RDF data with a standardized vocabulary to allow for entailment, e.g., type inheritance or type inference. Processing large RDF data sets, which are commonly stored as text files, is a time-intensive task: querying and entailment on RDF data requires a huge amount of computational power and storage. We propose TripleID – a framework for RDF querying and entailment processing. TripleID provides a novel, compressed file format for RDF data and utilizes Graphics Processing Units (GPUs) for accelerated, highly parallelized data processing. We demonstrate the advantages of our framework on real-world RDF data: TripleID reduces storage size for RDF data by up to 75% and accelerates querying and entailment processing up to 40 times as compared to the state-of-the-art tools that use conventional CPUs.