In 2025, selecting the optimal GPU for running GPT-OSS models, which include open-source reasoning models like the 20 B- and 120 B-parameter variants, involves balancing performance, cost, hardware specifications, and software optimizations. Key players in the GPU market, such as NVIDIA and AMD, offer models like the B200, H200, H100, and MI300X, each with varying capabilities in memory, throughput, and energy efficiency. NVIDIA's B200 leads in performance due to its dual-chip design and FP4 precision, which significantly enhances throughput and energy efficiency, while AMD's MI300X is a competitive alternative with strong scaling capabilities. Clarifai's Reasoning Engine further optimizes inference by utilizing speculative decoding and adaptive routing, significantly reducing costs and latency. Emerging technologies like FP4 precision and speculative decoding are transforming the landscape by improving efficiency and reducing energy consumption. Organizations can maximize throughput through multi-GPU setups and software strategies like expert and tensor parallelism. As the market evolves, new GPU architectures and precision formats, such as FP3, are anticipated to offer even greater performance improvements, necessitating ongoing adaptation to these advancements for sustainable and cost-effective AI deployment.