Why your local LLM feels dumber than it is
Points and comments are a snapshot, not live.
Software and hardware differences in local LLM inference create subtle output divergences that degrade perceived intelligence.
The author benchmarks Qwen3.6-27B across multiple attention backends (FlashAttention 2, Flash Inference, Triton) and weight quantizations (BF16, FP8, INT8, NVFP4, AWQ INT4) using a real 100k-token agentic workload. Triton and FlashAttention 2 show top-1 token flip rates approaching 10% at 96k context. KV cache quantization to INT8 or INT4 causes tool-calling failures: INT4 cannot recover from errors after 40k+ tokens. The NVIDIA NVFP4 quant reaches ~50% token flips by 88k context, while a community INT8 W8A16 quant matches BF16 most closely. These divergences stem from different CUDA kernels and GEMM operations across backends and quantizations, not from model quality itself.
What commenters are saying
Many users report qwen3.8 27b on Apple Silicon (M4, M5) is surprisingly smart for a local model, achieving 13-20 tok/s on 48GB machines. But heat and fan noise are common complaints: GPUs reach 95°C on M1 Max without forced fan control, and several warn of accelerated battery degradation. Several commenters note that reducing the reasoning_effort setting from xhigh to medium prevents the model from spinning indefinitely. Others say low-RAM users (8GB VRAM) find Gemma 4 more reliable than Qwen at small context lengths. A few argue the 'feels dumb' problem is real even at FP16 with 128GB when context exceeds 80k.