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$KIMI K3 CRAMS 2.78T PARAMETERS INTO 8GB RAM — PURE CPU, NO GPU! ⚡

That 176KB C99 script making headlines? It streams Kimi K3's 1.56TB of weights off NVMe instead of holding them in memory. 🔍 Classic MoE sparse activation — only 16 of 896 experts per layer actually fire. 💡

It's a liquidity sweep in reverse: the system pulls weights on demand like a trader pulling orders off the book. 🦈 Cost? 32.7 seconds per token. That's a prototype, not production — the dev says so himself.

But for AI-crypto watchers, this is a compass. If inference can run on 8GB with disk streaming, compute costs stop being a bottleneck — and that's a narrative shift. 📊 Which AI infrastructure play rides that wave first? 👇

⚠️ Not financial advice. Always manage your risk. 🛡️

🏷️ #KIMI #AI #MoE #Innovation #CryptoAI

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🚨 $KIMI SHRINKS 2.78T PARAMETERS INTO 8GB RAM — WITHOUT A GPU! 💥

🔍 This open-source experiment challenges every assumption about inference scaling. A 176KB pure C99 project runs Kimi K3's 1.56TB weight set on just 8GB of memory by streaming expert weights from NVMe storage on demand. 💡

The magic is MoE sparse activation — only 16 of 896 experts are active per layer. So the developer trades flash storage bandwidth for RAM, treating disk like a slow extension of memory. 📊 The tradeoff is brutal: one token every 32.7 seconds. That's not production-ready, but it's a structural blueprint for ultra-low-cost inference infrastructure.

The bigger signal is direction. 📉 As hardware bottlenecks force creative engineering, efficiency becomes the next narrative. Are we building the plumbing for the next wave of decentralized AI? 💬

⚠️ Not financial advice. Always manage your risk. 🛡️

🏷️ #KIMI #AI #MoE #OpenSource #Crypto

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Step 3.7 Flash by Lianjie Xingchen: 198B Parameter MoE Model, Focused on Production-Grade Agents

Lianjie Xingchen has officially launched and open-sourced the new generation Flash model, Step 3.7 Flash, under the Apache 2.0 license. This model features a sparse MoE architecture composed of a 196B language backbone and a 1.8B visual Transformer, totaling 198B parameters with only 11B activated. It supports 256K context and 3-tier inference, optimized for production-grade agents, coding, online search, and multimodal workflows.

Why it matters: The Chinese AI team has made significant breakthroughs in the open-source multimodal MoE model field, with a low activation parameter and high capability density architecture design representing the direction for efficient inference in the industry.

#AI #开源 #大模型 #MoE #ArtificialIntelligence