Hourly ·
DeepSeek's DSpark Hits #1 on Hacker News — 50-600% Faster Speculative Decoding
DeepSeek open-sources DSpark, a speculative decoding method that speeds up LLM inference by up to 600%, hitting #1 on HN and Reddit within hours of release.
DeepSeek released DSpark on Saturday — an open-source speculative decoding method that accelerates language model inference by 50% to 600%, depending on the workload. Within hours, the paper hit #1 on Hacker News with 508 points and spread across Reddit's AI communities.
DSpark works by predicting multiple future tokens in parallel and verifying them against the base model, dramatically reducing the per-token latency that bottlenecks real-time applications. It's compatible with DeepSeek V4 Pro and can be adapted to other architectures.
The release comes as the US-China AI bifurcation deepens. While Washington restricts Anthropic's Mythos exports, the global AI community is increasingly looking to open-source Chinese models that run faster, cost less, and aren't gated by geopolitics.
The code and paper are available on GitHub under DeepSeek's DeepSpec project.
Sources: GitHub (DeepSpec/DSpark), HuggingFace, Together.ai
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