While many companies have already successfully adopted AI workloads, it’s safe to say that in one of the most dynamic technological environments in history, most AI-equipped organizations will still be in the early stages of their AI journey for the foreseeable future. The capabilities of AI stacks are expanding at a breathtaking pace. For many businesses, keeping up with the whirlwind of AI-related change won’t just be a matter of exploring the cutting edge, it will be the way to maintain a competitive edge. If you’re a decision maker responsible for investments in AI infrastructure, you need the ability to cut through competing claims and make purchasing decisions with data-backed confidence. For critical hardware components like GPUs, a forward-looking evaluation should include criteria like real-world generational performance impacts, proven scale-out efficiency for future growth, and performance consistency across diverse OEM, ODM, and CSP ecosystems. For IT leaders making GPU procurement decisions, MLPerf Inference benchmark results can help inform smart investments.
We examined published AMD- and OEM-submitted MLPerf Inference benchmark results for two models of AMD Instinct GPUs—MI355X and the older MI325X—to see how those two representatives of the Instinct platform hold up on three fronts: generational performance gains, efficiency at scale, and consistency across a variety of ecosystems.
Based on the results we analyzed, we found that the newer AMD Instinct MI355X GPU outperformed the previous-generation MI325X GPU on Llama2-70b-99.9 Server throughput, a generational gain confirmed by both AMD-submitted and independent OEM-submitted results. When scaled out to an 11-node cluster, the MI355X broke the one-million-tokens-per-second mark, with each node retaining an average of 92 percent of its single-node performance, pointing to near-linear scaling efficiency. Additionally, we found that across nine OEM and solution partners who submitted MLPerf results using AMD Instinct GPUs—including Dell, Cisco, and Supermicro—the results landed within 3.5 percent of AMD's own submissions.
Together, these results demonstrate that AMD Instinct GPUs deliver generational performance gains, can scale efficiently across multi-node deployments, and produce consistent results across OEM hardware. For organizations planning GPU-enabled inference deployments, that combination of generational improvement, scaling efficiency, and cross-vendor reproducibility can offer confidence that choosing Instinct GPUs can translate to predictable performance in production environments.
For more details about our AMD Instinct GPU MLPerf Inference benchmark results analysis, check out the report and infographic below.
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