MTP on Qwen 3.6 27B: Coding speeds up 171%, but creative tasks slow down

The study tested Qwen 3.6 27B across four task types, five quantization levels, three temperatures, and multiple draft token counts. The core finding: task type dominates. For coding tasks (writing functions, debugging, refactoring), draft token acceptance rates hit 79–89%, leading to massive speedups. F16 quantization, normally slow at 6.6 tok/s, gains up to 171% with MTP. Q8_0 also benefits across all tasks (48–123% faster). But for creative writing (stories, poetry, roleplay), acceptance rates drop to 39–48%, and lower quants like Q4_K_M actually slow down by 9%. Analysis tasks show marginal gains or slight slowdowns on low quants.

Optimal draft tokens is N=3 for most scenarios; only F16 benefits from N=4. Temperature and MTP quantization (q8 vs matching model quant) barely affect results. The memory bandwidth bottleneck explains the pattern: F16 models saturate bandwidth, so each accepted draft saves an expensive full-model pass. But faster quants (Q4_K_M at 16GB) already decode quickly, and the overhead of generating and verifying draft tokens outweighs the benefit on less predictable tasks. Practical recommendation: always use MTP for coding at any quant; use MTP for creative tasks only on F16 or Q8_0 models.