A head-to-head comparison on a MacBook Pro M5 Max demonstrated that the 284 billion parameter DeepSeek-V4 Flash model, quantized to 2-bit, outperformed the 106 billion parameter GLM-4.5-Air model, which was quantized to 6-bit. Despite assumptions that higher precision would yield better results, DeepSeek-V4 Flash was faster and correctly solved a reasoning riddle where GLM-4.5-Air faltered. Both models performed equally on coding tasks, creative writing, and a common trick question, suggesting that for local, offline use on high-memory machines, model size can be more impactful than precision. AI
IMPACT Demonstrates that highly quantized large models can be run effectively on local hardware, increasing accessibility for AI operators.
RANK_REASON Comparison of two large language models on consumer hardware. [lever_c_demoted from research: ic=1 ai=1.0]
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