Ollama vs llama.cpp: Default settings, not engine, cause speed gap

A performance comparison between Ollama and llama.cpp on the same GGUF model revealed that the initial 15% speed difference was due to default settings rather than the underlying engine. When both applications were configured with identical draft and sampling parameters, their performance became nearly equal. Ollama's default use of multi-token prediction (MTP) speculative decoding with two draft tokens contributed to its higher speed in the initial test. AI

IMPACT Highlights the importance of consistent configuration for accurate LLM performance benchmarking.

RANK_REASON Comparison of two software tools for running LLMs.

Read on dev.to — LLM tag →

• CUDA
• GGUF
• llama.cpp
• Ollama
• Q4_K_M
• Qwen3.6 35B-A3B
• RTX Spark
• Surface Laptop Ultra
• Windows On Arm

• infra

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