If you’ve been tuning your GPU for gaming for years, you’ve probably focused more on pushing the core clock to push your framerates higher, with some undervolting thrown in for lower thermals. That may work for gaming, but local AI models don’t respond to core clock boosts as much as they do to an increase in the memory clock. LLMs thrive on memory bandwidth and capacity to accelerate data transfer in and out of the VRAM. Instead of compute-heavy workloads like gaming, LLM inference is memory-intensive, depending more on memory clock, bus width, and bandwidth. Of course, you also need a relatively newer GPU architecture and sufficient memory capacity to avoid stalled LLM workloads. The next time you are optimizing your GPU for LLM inference, focus on the memory clock instead of the core clock.