In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.
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How often does the "slow path" actually trigger? With 32 TLB entries covering 128 KB, Intel claimed a 98% hit rate for typical workloads of the era. That sounds impressive, but a 2% miss rate means a page walk every 50 memory accesses -- still quite frequent. So the 386 overlaps page walks with normal instruction execution wherever possible. A dedicated hardware state machine performs each walk:
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