Descriptions:
Fahd Mirza runs a live, unscripted comparison between Qwen3.6-27B and a community fine-tune called Thinking Cap — a variant of the same 27 billion parameter model trained specifically to compress its chain-of-thought reasoning without degrading answer quality. Both models are served locally on an NVIDIA RTX 6000 with 48 GB of VRAM using llama.cpp, with each running as a separate endpoint on different ports so identical prompts can be submitted simultaneously.
The test methodology is transparent: Mirza measures actual reasoning token counts from the JSON output of each model across progressively harder prompts, starting with a classic sheep-counting riddle and moving to a complex nested SQL query. On the riddle, Thinking Cap used 473 reasoning tokens versus the base model’s 584 — a roughly 30% reduction — while producing an identical correct answer. The harder SQL test is designed to stress the reasoning process more directly, where the claimed average reduction of around 50% (and up to 90% on some tasks) is more likely to manifest.
For practitioners running open-weight models locally or at inference scale, the video offers a reproducible template for evaluating efficiency fine-tunes: same model weights, different reasoning behavior, measurable token savings. The Q4_K_M quantization keeps both models within a single 48 GB GPU’s VRAM at around 37 GB combined, making the setup accessible to anyone with a high-end prosumer or workstation GPU.
📺 Source: Fahd Mirza · Published July 08, 2026
🏷️ Format: Benchmark Test
