A recent analysis by Leaseweb benchmarks the performance of AMD EPYC 9334 CPUs for Large Language Model (LLM) and Text-to-Speech (TTS) inference workloads. The study reveals that while GPUs offer higher throughput, CPUs can be a cost-effective and predictable option for inference, particularly when considering factors like latency and cost per query. The benchmarks highlight the impact of quantization, with Q4 models showing significantly better throughput on CPUs compared to FP16, and also compare performance metrics like Time to First Token (TTFT) and tokens per second (tok/s) against a reference Nvidia L4 GPU. AI
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IMPACT CPU inference can offer a more predictable and cost-effective alternative to GPUs for certain LLM and TTS workloads, especially at scale.
RANK_REASON The article presents benchmark results and analysis of hardware performance for AI inference workloads. [lever_c_demoted from research: ic=1 ai=0.7]