Qwen 3.6 27B BF16 vs Q4_K_M vs Q8_0 GGUF evaluation
Q4_K_M drops only 3% accuracy but cuts RAM by 48% and runs 1.45x faster.
A comprehensive evaluation of Qwen 3.6 27B across BF16, Q4_K_M, and Q8_0 GGUF quant variants reveals Q4_K_M as the standout for local deployment. Using llama-cpp-python and the Neo AI Engineer framework, the test covered 664 total samples across HumanEval (code generation), HellaSwag (commonsense reasoning), and BFCL (function calling). BF16 achieved the highest average accuracy at 69.78%, with 15.5 tok/s throughput, but required 54 GB peak RAM and a 53.8 GB model file—impractical for most local setups. Q4_K_M delivered 66.54% average accuracy, with BFCL scores nearly identical to BF16 (63.00% vs 63.25%), and only minor drops in HumanEval (50.61% vs 56.10%) and HellaSwag (86.00% vs 90.00%). Its 22.5 tok/s throughput, 28 GB RAM, and 16.8 GB model size make it the most efficient option. Q8_0 performed underwhelmingly: 66.15% accuracy, slower 18.0 tok/s, and 42 GB RAM. For local or CPU deployment, Q4_K_M is recommended unless code generation is the primary workload, where BF16 still leads.
- Q4_K_M achieves 66.54% accuracy with 22.5 tok/s, 28 GB RAM, and 16.8 GB model size—48% less RAM and 1.45x faster than BF16.
- BF16 tops accuracy at 69.78% but requires 54 GB RAM and a 53.8 GB model, while Q8_0 disappoints with 66.15% accuracy and 42 GB RAM.
- Function calling scores are nearly identical across all variants: BF16 at 63.25%, Q4_K_M and Q8_0 both at 63.00%.
Why It Matters
Q4_K_M quantization enables powerful 27B models to run locally on consumer hardware with minimal accuracy loss.