DSpark Speculative Decoding for LLM Memory Bottlenecks
We've spent the last two years obsessing over FLOPS and H100 clusters, but the real bottleneck for LLMs isn't actually compute. It's memory. The GPU spends most of its time just waiting for weights to move from memory to the cores. It's a massive waste of silicon. DSpark tries to fix this by flipping the script. Instead of blindly crunching the next token, it uses a tiny draft model to essentially guess what's coming next. If the guess is right, the big model just verifies it and moves on. It's a clever bit of speculation that treats the LLM more like a judge and less like a typewriter. The results are interesting, but it raises a question about the architecture we've settled on. If a tiny model can predict the output of a giant one with high accuracy, are we just over-provisioning our inference for the sake of a few edge cases? The Memory Bandwidth Bottleneck Autoregressive decoding is slow because it's a memory bandwidth problem, not a comp...