How AI Got 19x Faster 🤯 | Multi-Token Prediction Explained (DeepSeek & Qwen)

Описание: AI models are getting insanely fast… but why?

The answer is Multi-Token Prediction (MTP) — the core technique behind models like DeepSeek-V3 and Qwen 3.5 that achieve up to 19x faster inference.

In this video, we break down how MTP works, why traditional next-token prediction is slow, and how modern architectures are solving this bottleneck.

🚀 *What you'll learn:*
• Why Next-Token Prediction (NTP) limits speed
• How Multi-Token Prediction forces models to plan ahead
• Meta’s Gloeckle architecture (parallel heads approach)
• DeepSeek-V3’s sequential MTP modules
• Qwen 3.5’s hybrid design with linear attention
• Self-Speculative Decoding (massive speed boost)

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🧠 *Why this matters:*
MTP is one of the biggest breakthroughs in LLM inference. It enables faster generation without sacrificing quality — a key step toward real-time AI systems.

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⏱️ *Timestamps:*
00:00 Introduction
00:14 The Bottleneck (Next-Token Prediction)
00:40 Multi-Token Prediction Concept
01:03 Meta Gloeckle Architecture
01:30 DeepSeek-V3 Approach
01:51 Qwen 3.5 Architecture
02:17 Self-Speculative Decoding
02:44 Summary

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