Comment by Const-me

> you can get a performance improvement by calculating A, B, and C in parallel, then adding together whichever two finish first

Technically possible, but I think unlikely to happen in practice.

On the higher level, these large models are sequential and there’s nothing to parallelize. The inference is a continuous chain of data dependencies between temporary tensors which makes it impossible to compute different steps in parallel.

On the lower level, each step is a computationally expensive operation on a large tensor/matrix. These tensors are often millions of numbers, the problem is very parallelizable, and the tactics to do that efficiently are well researched because matrix linear algebra is in wide use for decades. However, it’s both complicated and slow to implement fine grained parallelism like “adding together whichever two finish first” on modern GPUs. Just too much synchronization, when total count of active threads is many thousands, too expensive. Instead, operations like matrix multiplications are often assigning 1 thread per output element or fixed count of output elements, and reduction like softmax or vector dot product are using a series of exponentially decreasing reduction steps, i.e. order is deterministic.

However, that order may change with even minor update of any parts of the software, including opaque pieces at the low level like GPU drivers and firmware. Library developers are updating GPU kernels, drivers, firmware and OS kernels collectively implementing scheduler which assigns work to cores, both may affect order of these arithmetic operations.