MTP inference speed issues in llama.cpp explained

A technical blog post explains why Multi-Token Prediction (MTP) in llama.cpp might not improve inference speed as expected. The author details three primary reasons for this performance issue: a low acceptance rate of predicted tokens, KV cache thrashing due to aggressive candidate generation, and CUDA graph capture failures when MTP introduces dynamic shapes. The post provides a step-by-step guide for diagnosing these problems, including measuring acceptance rates, monitoring VRAM usage, and testing inference with CUDA graphs disabled. AI

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IMPACT Provides practical guidance for optimizing LLM inference performance on local hardware.

RANK_REASON Technical blog post detailing performance tuning for a specific software library.

Read on dev.to — LLM tag →

• CUDA
• llama.cpp
• Multi-Token Prediction
• Qwen3

• infra
• other