We Just Implemented This And Got A Free 20% Speedup On AI! ~ Training-Free Multi-Token Prediction Makes LLMs 15–26% Faster Researchers at Qualcomm AI Research have released a breakthrough inference technique that dramatically speeds up LLMs, with zero retraining, zero extra parameters, and zero quality loss. The paper “Efficient Training-Free Multi-Token Prediction via Embedding-Space Probing” shows how to predict multiple future tokens in parallel by dynamically probing the model’s own embedding space with smart “mask tokens.” Speedup Highlights • 15–19% higher throughput on LLaMA3.1-8B, Qwen3, and similar models • Up to 26% throughput gains with simple optimizations • Example: 38.9 → 40.5+ tokens/second on LLaMA3.1-8B • Up to 40% fewer model forward passes It’s completely plug-and-play and works on any frozen autoregressive LLM while producing identical outputs to standard decoding. Beats other training-free baselines (Lookahead Decoding, Prompt Lookup) by 24% in acceptance rate and throughput • Up to 40% fewer model forward passes • Lossless identical outputs to normal decoding • Ideal when you want faster LLMs today with zero extra cost or complexity Perfect for local AI, edge devices, mobile apps, real-time chat, and slashing cloud inference costs. We are running it now on all models and absolutely increased JouleWork outputs. • PDF: