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David Hendrickson
@TeksEdge
CEO & Founder | PhD | Startup Advisor | @Columbia | Author Generative Software Engineering | 🔔 Follow for AI & Vibe Coding Tips 👇
加入 July 2023
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Intel's 32GB Arc Pro B70 finally got a proper Qwen3.8-27B Local AI workout. So this is why the 32GB VRAM is important, even from an Intel GPU. Here is Gigazine's setup. Windows 11 using Unsloth Desktop + llama.cpp ... 🧠 Qwen3.8-27B Q4_K_XL 💾 30.0GB VRAM + ~1GB shared RAM 🚀 23.4 tps Then they tried Q8: 🧠 Qwen3.8-27B Q8_K_XL 💾 30.2GB VRAM + ~1.5GB shared RAM ⚡ 15.9 tps That's a full 27B-class model running at very usable speeds on an Intel GPU. And also tested text-to-image models. 🎨 Z-Image-Turbo 1024×1024 / 8 steps ➡️ ~5.3 sec/image after warmup 🎥 MiniMax H3 video also ran …but these are much slower, demonstrating where Nvidia's more mature AI software stack still matters. The hardware setup... 🎮 Arc Pro B70 💾 32GB GDDR6 ⚡ 608 GB/s bandwidth 🔌 230W And here's the interesting value angle. In Japan, the tested ASRock B70 was selling for: 💰 ¥298,054 while many RTX 5090s were selling above: 💰 ¥900,000 ⚠️ That's Japan-specific street pricing, NOT a universal B70-vs-5090 price comparison. But THIS is why the B70 interests me for Local AI. It won't beat a 5090 but it's giving you 32GB of VRAM at a much lower entry point. 23.4 tok/s on Qwen3.8-27B Q4 good enough for you? 🔗 GIGAZINE review in ALT
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