MTP (Multi-Token Prediction): 2x Faster Token Generation on AMD Strix Halo & Radeon 9700 AI Pro
Summary
MTP (Multi-Token Prediction) can accelerate LLM inference by 2x, especially for coding agents. This video demonstrates performance improvements with Qwen 3.6 on AMD Strix Halo and Dual Radeon 9700.
Similar Articles
I tested MTP on vLLM and llama.cpp for Gemma 4 & Qwen 3.6 — 3.34x faster inference, here are my findings RTX 6000 PRO.
Benchmarks of Multi-Token Prediction (MTP) on Gemma 4 31B and Qwen 3.6 27B using vLLM and llama.cpp show up to 3.34x faster inference, with optimal speculative token counts varying by model and engine.
MTP+GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 - llama.cpp
A user benchmarks token generation speed on llama.cpp with the GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 flag, comparing performance with and without MTP (Multi-Token Prediction). Results show a significant speedup from 49 tok/s to 64 tok/s when MTP is enabled on an RTX5090 with a Qwen3.6-27B model.
Strix Halo Llama.cpp MTP Benchmarks: 27B Gets Much Faster, 35B Is Mixed
Benchmarks of MTP (Multi-Token Prediction) in llama.cpp on Strix Halo show significant speedups for 27B Qwen models in long-context chat, but mixed results for 35B models.
MTP is all about acceptance rate
A user benchmarked MTP (Multi-Token Prediction) on Gemma 4 with mlx-vlm on M4 Max Studio, finding it excellent for code generation (1.53x faster, 66% acceptance) but detrimental for JSON output (50% slower, only 8% acceptance) and neutral for long-form prose, suggesting MTP benefits vanish when acceptance drops below 50%.
Getting the most out of MTP
A guide on optimizing MTP (Multi-Token Prediction) performance by tuning n_max parameter, with benchmark results for various models like Gemma-31b and Qwen on P100 and V100 GPUs.