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The increasing difficulty in leveraging CMOS scaling for improved performance requires exploring alternative technologies. A promising technique is to exploit the physical properties of devices to specialize certain computations. A recently proposed approach uses molecular-scale optical devices to construct a Resonance Energy based Sampling Unit (RSU) to accelerate sampling from parameterized probability distributions. Sampling is an important component of many algorithms, including statistical machine learning. This paper explores the relationship between application result quality and RSU design. The previously proposed RSU-G focuses on Gibbs sampling using Markov Chain Monte Carlo (MCMC) solvers for Markov Random Field (MRF) Bayesian Inference. By quantitatively analyzing the result quality across three computer vision applications, we find that the previously proposed RSU-G lacks both sufficient precision and dynamic range in key design parameters, which limits the overall result quality compared to software-only MCMC implementations. Naively scaling the problematic parameters to increase precision and dynamic range consumes too much area and power. Therefore, we introduce a new RSU-G microarchitecture that exploits an alternative approach to increase precision that incurs 1.27x power and equivalent area, while maintaining the significant speedups of the previous design and supporting a wider set of applications.