With the emergence of highly heterogeneous, dynamic and large distributed platforms, declarative programming, whose goal is to ease the programmer's task by separating the control from the logic of a computation, has regained a lot of interest recently, as a means of programming such platforms. In particular, rule-based programming is regarded as a promising model in this quest for adequate programming abstractions for these platforms. However, while these models are gaining a lot of attention, there is a demand for generic tools able to run such models at large scale. The chemical programming model, which was designed following the chemical metaphor, is a rule-based programming model, with a non- deterministic execution model, where rules are applied concurrently on a multiset of data. In this paper, we explore the experimental side of concurrent rule-based models, by deploying a distributed chemical runtime at large scale. The architecture proposed combines a peer-to-peer communication layer with an adaptive protocol for atomically capturing objects on which rules should be applied, and an efficient termination-detection scheme. We describe the software prototype implementing this architecture. Based on its deployment over a real-world test-bed, we present its performance results, which confirm analytically obtained complexities, and experimentally show the sustainability of such a programming model.