LongCat 2.0: N-Grams Beat More Experts

LongCat 2.0: How Meituan Trained a 1.6T Model Without NVIDIA GPUs or TPUs In this video, I break down how Meituan open sourced LongCat 2.0 (Owl Alpha on OpenRouter), a 1.6T-parameter MoE model trained without NVIDIA GPUs or Google TPUs, and why that matters for reducing reliance on NVIDIA’s hardware and CUDA software stack. I explain the core tradeoffs between parameters and compute, why simply adding more experts can hit diminishing returns, and how LongCat uses n-gram embeddings to increase corpus information more cheaply than adding experts. I cover the long-context cost problem and their modified sparse attention approach (inspired by DeepSeek) that makes the helper lighter via predictable memory access, caching across layers, and coarse-to-fine selection. I also discuss speculative decoding with a draft “picker” model, custom ASICs tuned for prefill vs decode, training on 50,000+ chips over 35T tokens, and I demo it generating a 3D ISS tracker on longcat.chat. https://longcat.chat/blog/longcat-2.0/ DeepSpec Video: https://youtu.be/eFgknPFK-g0 My voice to text App: whryte.com Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 Let's Connect: 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: [email protected] Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 00:00 LongCat Shockwave 00:39 Why Hardware Matters 01:20 MoE Limits and N-Grams 03:40 Cheaper Long Context 06:23 Custom Chips and Stack 08:23 Demo Results and Takeaways