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In voice systems, receiving the first LLM token is the moment the entire pipeline can begin moving. The TTFT accounts for more than half of the total latency, so choosing a latency-optimised inference setup like Groq made the biggest difference. Model size also seems to matter: larger models may be required for some complex use cases, but they also impose a latency cost that's very noticeable in conversational settings. The right model depends on the job, but TTFT is the metric that actually matters.
Plotting instruction counts shows just how severe the situation can get for Cortex X925. 507.cactuBSSN, 521.wrf, 549.fotonik3d, and 554.roms all require more instructions on X925, and by no small margin. 554.roms is the worst offender, and makes X925 execute more than twice as many instructions compared to Zen 5. Average IPC in these four tests is nowhere near core width for any of these tested cores, but crunching through extra instructions isn’t the only issue. Higher instruction counts place more pressure on core out-of-order resources, impacting its ability to hide latency.