Running local LLMs often feels frustratingly slow despite decent output quality, leading users to endlessly chase bigger models. The real fix is speculative decoding — a technique where a smaller draft model predicts tokens ahead and the main model only verifies them, dramatically reducing wasted computation. This single change can make the same model feel significantly faster and more usable without any hardware upgrades or model swaps.
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Running a local LLM is easy until you actually try to use it every dayI thought the answer was a better modelThe tweak that changed everything was speculative decodingSpeculative decoding matters more than most tuning settingsSort: