@diy_smart_code: NVIDIA quantized GLM-5.2 to 4-bit with a new format called NVFP4 and posted it on HuggingFace — but HuggingModels' "consumer hardware" post about a 753B-parameter mixture-of-experts model running only on NVIDIA Blackwell has the internet roasting it. ---- 🚀 DYNAMOUS AI COMMUNITY Want to learn agentic coding with live daily events and workshops? Check out Dynamous AI: https://dynamous.ai/?code=646a60 Get 10% off here 👉 https://shorturl.smartcode.diy/dynamous_ai_10_percent_discount ⚡ HOSTINGER — RELIABLE HOSTING FOR YOUR PROJECTS (10% OFF) Whether you're shipping a portfolio, a side project, n8n flows, or AI agents — I use Hostinger for fast, affordable VPS + web hosting. Get 10% off here 👉 https://hostinger.com/DIYSMARTCODE (Affiliate link — costs you nothing, supports the channel.) ---- What you will see in this breakdown: → NVFP4 explained — NVIDIA's 4-bit quantization format that shrinks model math for smaller, faster, cheaper inference → GLM-5.2's real specs — a mixture-of-experts model, 753 billion parameters total, only 40 billion active per token → Built for coding agents, chatbots, and RAG systems → The hardware twist — supported hardware is NVIDIA Blackwell only, with an 8-GPU runtime example → The internet's reaction — "consumer hardware," and it's just 400GB of VRAM → The receipt — 4x DGX Spark hits 28-38 tokens/sec single stream, a datacenter benchmark on datacenter machines → What this quantization trick actually means for your API bills Model card: https://huggingface.co/nvidia/GLM-5.2-NVFP4 Hugging Models called 400 gigs of VRAM "consumer hardware." Would you? #GLM52 #NVIDIA #NVFP4 #Quantization #AIHardware #Blackwell #HuggingFace #MixtureOfExperts #LLM #AIModel #MachineLearning #DGXSpark #AIInference #OpenSourceAI #AICoding #RAG #AIAgents #TechNews #AIExplained #DeepLearning #GPU #AIRoast #ModelCard #AITools

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Tuesday 07 July 2026 11:46:23 GMT