The efficiency of neural information retrieval methods is primarily evaluated by measuring query latency. In practice, measuring latency is highly tied to hardware configurations and requires extensive computational resources. Given the rapid introduction of retrieval models, achieving an overall comparison of their efficiency is challenging. In this paper, we introduce PEIR, a framework for hardware-independent efficiency measurements in Learned Sparse Retrieval (LSR). By employing performance modeling approaches from high-performance computing, we derive performance models for query evaluation approaches such as BlockMax-MaxScore (BMM) and propose to measure memory and/or floating-point operations while performing retrieval on input queries. We demonstrate that by using PEIR, similar conclusions on comparing the latency of retrieval models are obtained.