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

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

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

在 dev.to — LLM tag 阅读 →

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

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