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| Metric | Value |
|---|---|
| Base model | Qwen/Qwen3.5-35B-A3B |
| File size | 11 GB |
| Quantization | IQ3_S custom-mix (base) + BF16 MTP tensors |
| Total layers | 41 (40 main + 1 MTP) |
| Total tensors | 753 |
| MTP acceptance | 84.8% (warm cache), 61-63% (cold) |
| Config | Speed | Acceptance | Notes |
|---|---|---|---|
Without -mtp | 94 tok/s | — | Normal inference |
-mtp --draft-max 1 | 47.6 tok/s | 84.8% warm / 61% cold | Works but slower |
-mtp (multi-step) | 12.7 tok/s | 12% | Acceptance collapse (known MTP issue) |
-ncmoe 4), each MTP forward costs nearly as much as a main model forward. The bottleneck is MoE expert transfer over PCIe, not the number of forward passes.1# Inject MTP tensors from original BF16 shards into IQ3_S base
2python build_mtp_gguf_v3.py
3# Source: IQ3_S custom-mix base + shards 13-14 from Qwen/Qwen3.5-35B-A3B