@DeRonin_: DeepSeek just dropped a 5-page paper + free GitHub repo that makes any LLM respond 80% faster it's called speculative d…
Summary
DeepSeek released a paper and MIT-licensed open-source implementation of speculative decoding (DSpark) that speeds up LLM responses by up to 80% by using a small 'guess' model and a large 'check' model, achieving both speed and accuracy without tradeoffs.
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Cached at: 06/27/26, 08:01 PM
DeepSeek just dropped a 5-page paper + free GitHub repo that makes any LLM respond 80% faster
it’s called speculative decoding. in plain english:
Guess → Check → Keep → Repeat
Guess: a small fast model predicts the next few words Check: the big smart model checks all guesses at once Keep: lock in the right ones, fix the first wrong one Repeat: do it again, 2-4x faster than normal
DeepSeek’s version (DSpark) is the first to combine two approaches that always had a tradeoff: getting both speed AND accuracy at the same time
production results on their flagship model: 50% more requests handled, responses up to 80% faster
how you can use it:
just using AI apps: expect them to get noticeably faster soon building with AI: integrate this into your stack for instant 2x speedups running your own models: clone the repo today and apply it to whatever model you run
every other AI lab has been guarding this. now it’s free on GitHub, MIT licensed
this completely changed how I’m thinking about AI products this week
one of the most powerful releases this month
repo + papers below ↓
here’s the full breakdown on how it works:
Github Repo: https://github.com/deepseek-ai/DeepSpec/tree/main… Paper: https://github.com/deepseek-ai/DeepSpec/blob/main/DSpark_paper.pdf…
Bro, why are you writing as a bot?
For what you’re doing that? It doesn’t give a credibility at all…
Gonna use it for my research engine in terms of scraping and speed in this case is crucial
Just try to pick the most optimal routting Way
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