🤯 Remember back in July I called Ling-3.0-flash one of the more interesting Local AI models? ... because Ling-3.0-flash combined 124B-scale capacity with only ~5B active parameters per token, strong frontierish benchmarks, and a design that looked promising for local agents. My July post ended with one BIG problem 👉 localmaxxers couldn't download the weights or load it into llama.cpp.” Well... that part changed. 🔥 @TheInclusionAI has now released the weights under MIT, an official GGUF exists for Ling-3.0-flash, and they've just released Ling-3.0-flash-VL. The new VL model adds: 👀 Images 🎥 Video 🤖 Visual/computer-use agents 🧠 124B total / ~5.5B active 📚 up to 1M context ⚖️ MIT A community Ling-3.0-flash-VL Q4_K_M GGUF is already ~79.3GB ~1.7GB vision projector. So roughly 81GB for a 124B multimodal MoE. That puts it in range of these devices ... 🧠 128GB Strix Halo 🍎 high-memory Apple Silicon 🎮 large/multi-GPU home rigs ⚠️ The VL GGUF is still experimental and currently uses a patched llama.cpp, so this is not yet a clean stock-llama.cpp experience. But this is exactly the update I wanted back in July, weights on our own machines. 🔥 Now somebody run that ~81GB VL stack on a 128GB Strix Halo and give us the tps. 👀 🔗 HF inclusionAI/Ling-3.0-flash-VL