Why your local LLM feels dumber than it is

This technical deep-dive from Level1Techs reveals why your local LLM may underperform despite using the same weights as the reference model. Through controlled experiments on Qwen3.6-27B, the author demonstrates that implementation details—such as attention backend, KV cache quantization, and weight quantization—cause measurable divergence in token probabilities, leading to subtle errors and even tool-calling failures. The post emphasizes that benchmarks must reflect real workloads and that methodology is crucial when interpreting KLD claims.