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ZenDNN

The Business Case for CPU-Based AI Inference

Your finance team doesn't care about tokens per second. They care about predictable costs, compliance risk, and vendor lock-in. Here's how CPU inference stacks up.

The other week I published a technical deep-dive on running LLM inference with AMD EPYC processors and ZenDNN. The benchmarks showed that a $0.79/hour VM can push 40-125 tokens per second depending on model size, genuinely usable performance for a surprising range of workloads.

But benchmarks don't answer the question that actually matters: Should you do this?

Running AI Inference on AMD EPYC Without a GPU in Sight

Spoiler: You don't need a $40,000 GPU to run LLM inference. Sometimes 24 CPU cores and the right software stack will do just fine.

The AI infrastructure conversation has become almost synonymous with GPU procurement battles, NVIDIA allocation queues, and eye-watering hardware costs. But here's a reality that doesn't get enough attention: for many inference workloads, especially during development, testing, and moderate-scale production, modern CPUs with optimized software can deliver surprisingly capable performance at a fraction of the cost.