An technical analysis demonstrates how inference engine configurations affect LLM output consistency for the Qwen3.6-27B model. The study used a 100k token context from a real workstream to reflect typical agent tasks. In the first test, three full‑attention backends—FlashAttention 2, Flash Inference, and Triton Attention—were run on a Blackwell GPU with BF16 weights and KV cache; token‑level logits were sampled every 32 tokens and compared for top‑1 agreement. Results showed high agreement at the start of the prompt but increasing divergence in later sections, with token flips varying by backend. The second test kept model weights and activations in BF16 while quantizing only the KV cache; int8 quantization allowed recovery, whereas int4 quantization caused tool‑call failures. The third test compared several weight‑quantization formats—FP8, INT8, NVFP4, and AWQ—while retaining BF16 KV cache; the W8A16 configuration achieved the lowest token‑flip rate, while NVFP4 and AWQ exhibited higher error rates and incorrect command‑line outputs in tool calls. The author notes that additional experiments require extensive GPU time and invites questions via direct message or Discord; the findings indicate that local LLM performance is highly dependent on hardware, software, and quantization settings.