Your 12GB MTP Throughput Just Jumped 23%
A community llama.cpp fork squeezes 110 tok/s out of Qwen3.6-35B-A3B MTP on a 12GB card. Here are the exact flags and the VRAM trick to make it fit.
#llama-cpp
8 posts
Your Local Throughput Just Doubled (With No Accuracy Tax)
Multi-token prediction is merged into llama.cpp, delivering 1.4–2.2x throughput on Qwen3.6 with zero accuracy loss.
MTP in llama.cpp Adds 24–33% Throughput on Qwen3
llama.cpp merged Multi-Token Prediction for Qwen3. Community benchmarks show 38→47 tok/s on RTX 3090 and 63→84 tok/s on RTX 5090 — no new hardware needed.
DFlash Decode Collapses at Long Output. MTP Doesn't.
One llama.cpp flag—--spec-draft-p-min 0.75—turns MTP from a dud into a decode speed that holds flat across output lengths where DFlash falls apart.
One llama.cpp Flag Turns MTP From Dead Weight to 68% Faster
The --spec-draft-p-min filter in llama.cpp PR #22397 rescues MTP for Qwen3.6-27B: 48.9 tok/s vs 29 tok/s at 2000 tokens on a 24GB card.
Your Local Qwen3.6 Throughput Probably Just Halved (and How to Fix It)
llama.cpp renamed the MTP flag on May 13. The old --spec-type mtp is silently ignored. If your tok/s dropped from 140 to 70 you are likely running without speculative decoding.
Speculative Decoding Explained: Why Your Local Model Got 2× Faster in 2026
The same Qwen3.6-27B that ran at 70 tokens/sec on a 4090 in January was running at 140 tokens/sec by April. Nothing changed about the model. Speculative decoding moved from research curiosity to default. Here is what it actually does.
Running Qwen3.6-27B Locally: Hardware, Quantization, and What Actually Works
A practical guide to running Qwen3.6-27B on consumer hardware in 2026 — memory requirements per quant level, recommended runners, and the MTP trick that doubles your tokens per second.