Large language models (LLMs) have established new standards in various natural language processing tasks. However, a primary constraint they face is the context limit, i.e., the maximum number of tokens they can process.To relax the constraint, previous works have explored architectural changes and modifications in positional encoding, but they often require expensive training or do not address the computational demands of self-attention.In this paper, we present Hierarchical cOntext MERging (HOMER), a new training-freescheme designed to overcome the limitations. HOMER harnesses a divide-and-conquer methodology, segmenting extensive inputs into manageable units. The segments are then processed collectively, employing a hierarchical strategy that fuses adjacent chunks at progressive Transformer layers. A token reduction technique precedes each fusion, ensuring memory usage efficiency.We also propose an optimized computational order reducing the memory requirement to logarithmically scale with respect to input length, making it especially favorable for environments with tight memory restrictions. Our experimental results demonstrate the superior performance and memory efficiency of the proposed method, opening doors for broader applications of LLMs in scenarios with extended context requirements.