AMD Optimizes BERT Inference with EPYC Processors and ZenDNN

AMD has unveiled performance benchmarks for low-latency CPU inference on its EPYC processors, leveraging optimizations in its ZenDNN library and zentorch PyTorch plugin. The tests, conducted in September 2026, focused on BERT-Base uncased inference using Hugging Face's implementation. The results highlight significant latency gains, particularly when using BF16 precision, with improvements reaching up to 58.1% compared to ONNX Runtime FP32. AMD's testing spanned two configurations: a 96-core EPYC 9654 bare-metal system with NPS4 and an 80-core EPYC 9V74 virtual machine with NPS5. Across 42 configurations, zentorch outperformed ONNX Runtime in 83% of cases, tied in 10%, and trailed in 7% on the bare-metal setup. In the virtual machine, zentorch achieved similar results, leading in 83%, tying in 7%, and trailing in 10% of configurations.

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