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Running LLMs Locally Does Not Mean Your Data Is Safe: What Cohere’s Study Reveals
Running a large language model locally is often considered the gold standard for developers and organizations seeking absolute control over their data. By downloading model weights and executing workloads entirely on proprietary hardware, users can avoid transmitting sensitive intellectual property, corporate secrets, or personal information to external AI providers. This air-gapped approach has formed the…
