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

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

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