Qwen 3.5 leads local LLM benchmarks after switch to llama.cpp

A technical blog post details a shift from using Ollama to llama.cpp for running large language models locally. The author found that Ollama, while user-friendly, introduced an abstraction layer that potentially skewed benchmark results. By migrating to llama.cpp, the author gained finer control over inference parameters, enabling more accurate benchmarking and optimization. This change led to Qwen 3.5 emerging as the top-performing model across coding and agentic tasks. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Optimized local LLM inference and benchmarking reveals superior performance of Qwen 3.5, potentially influencing future model selection and deployment strategies.

RANK_REASON Technical deep-dive into optimizing LLM inference and benchmarking methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• Qwen 3.5
• llama.cpp
• Ollama
• Gemma 4
• Kimi K2
• HuggingFace

• infra
• model release