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  "text": "Double down on this. 💯\n\nTake OCR and document parsing for instance. Running a giant monster model is pure overkill.\n\n1/ Data > Model Size: A lightweight, fine-tuned VLM (or custom parsing model) fed with high-quality document data easily beats frontier giant LLMs in pixel-perfect structural extraction.\n\n2/ The ROI Problem: Invoices, financial reports, and endless PDFs need high throughput. Good luck processing them at scale with hyper-expensive API bills or massive GPU clusters.\n\n3/ Local & Edge: Edge deployment (even via llamacpp/ONNX) with zero data leak risks is what enterprises actually need.",
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    "text": "A study from @Stanford showed that 71.3% of chatgpt queries could be accurately answered by a local model. I suspect a major part of enterprise AI workloads could be run locally too for free (compared to the massive costs of frontier API cost). \n\nAlso, it reduces the risk of these workloads being taken away from you because you own the models instead of renting them - which sounds like a good idea these days haha.\n\nThat's why we're introducing the ability for everyone to filter AI models on @huggingface based on your local hardware. \n\nFor me, there are 800k+ public models that fit on my M5 24GB and that I can use easily thanks to llamacpp.\n\nLet's go local AI!",
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