🤯 Remember the weird U.S.-built diffusion LLM I posted about that broke 1,000 tok/s on Nvidia GPUs? It got a BIG upgrade. @_inception_ai released Mercury 2.5 today. 🎯 Instead of generating one token after another like a normal LLM, Mercury uses diffusion to generate/refine multiple tokens in parallel. And Inception says the new model delivers the following 👇 🚀 1,107 tok/s 👀 🧠 +40% intelligence vs Mercury 2 📚 260K context 🤔 Tunable reasoning 🔧 Parallel tool calling 📐 Structured JSON 💵 $0.20/M input / $0.75/M output 👀 🎯Inception says this is the largest diffusion language model ever trained. That ~1,100 tok/s isn't coming from Groq or Cerebras-style custom inference hardware - this is an important point! It's running on widely available NVIDIA GPU infrastructure. That's the big architectural bet, traditional LLM 👇 token → token → token → token Diffusion LLM 👇 many tokens → refine them together → answer ⚠️ Caveat, of course! 🔒 Mercury 2.5 is closed-weights.