AMD EPYC 9575F and Instinct MI300X: Why even the best AI GPU is just twiddling its thumbs without a powerful CPU

Sometimes you don’t need a new architecture, you just need common sense – or as AMD calls it: a “balanced platform”. Sounds like yoga for servers, but in reality it’s the bitter realization that a high-end GPU like the MI300X runs as efficiently in a weak CPU setup as a Formula 1 engine in a shopping cart. Welcome to the world of EPYC and the big clean-up in the AI infrastructure.

CPU as a bottleneck – who would have thought it?

At the latest Advancing AI event, AMD not only showed a few nice diagrams, but also reorganized the entire server ecosystem. In a direct comparison: Intel Xeon 8592 (5th gen after all) vs. AMD EPYC 9575F – each combined with the Instinct MI300X. So same GPU, same workloads, identical conditions – apart from the fact that Intel’s CPU is already two years old. But be that as it may, according to AMD, only the “balance game” counts. And lo and behold: In inferencing with Llama 3.1 (8B), the EPYC host is 6% ahead on average. With larger models, the lead increases to up to 17%. Not revolutionary, but relevant – especially when latency and energy consumption play a role. AMD cleverly exploits this: “The right CPU = better AI”, the formula is that simple. Those who ignore the bottleneck will be punished by the power meter.

Latency vs. reality: Xeon becomes a brake pad

Particularly bitter for Intel: with a 300 ms latency limit, the Xeon system doesn’t even manage to deliver any significant results according to AMD – the EPYC, on the other hand, dances through the inference tests like a ballerino on Red Bull. We are talking about ten times better goodput under latency pressure. A performance humiliation with an announcement – and a big question mark over Intel’s platform strategy.

Energy efficiency: AMD’s new favorite

AMD also scores points in the “Greta metrics”: In combination with the MI300X, the EPYC 9575F delivers an almost 30-fold increase in efficiency compared to 2020. Sounds like something from the ESG advertising department, but it’s certainly verifiable – at least if you believe AMD’s metrics. If you pay less electricity, you can install more accelerators. Simple calculation model for CFOs with a thrifty mindset.

Intel counters – but how?

Of course, Intel is not remaining silent. In a mixture of benchmark voodoo and “you haven’t optimized properly” arguments, they try to put AMD’s results into perspective. By pointing to its own AMX accelerators and better software (proprietary, of course), Xeon is suddenly supposed to come up trumps again. Sounds a bit like “my compiler wasn’t awake”. But let’s be honest: if you build your AI future on a patchwork of platforms, you won’t be surprised by poor latency or high TDP in the end. AMD delivers an overall package that not only impresses with its performance, but also with its consistency – from the CPU to the bandwidth to the coherence scheme.

The truth is in the node

What AMD is showing here is not a revolutionary “David versus Goliath” tale, but a sober data center scenario: Anyone building AI infrastructure today not only needs GPUs with biblical memory, but also a CPU that does not collapse with every model change. EPYC 9575F currently seems to offer the more well-rounded package for this. Intel must counter – with better platform logic, a more open software strategy and perhaps also a pinch of humility. Because as long as accelerators are slowed down by CPUs at full speed, every AI benchmark remains a double-edged sword.

Source: Youtube