I benchmarked two local LLMs on agentic coding tasks — the results surprised me
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Benchmarking local LLMs on agentic coding tasks reveals surprising performance gaps between models
Action Steps
- Run QuantaMind's Coding eval suite on local LLMs to assess their performance on agentic coding tasks
- Configure the eval suite to use the Easy tier difficulty setting
- Compare the performance of different LLM models, such as Ollama models, on the same machine and backend
- Evaluate the results to identify the gaps in performance between models
- Use the insights from the benchmarking to fine-tune and improve the performance of local LLMs
Who Needs to Know This
AI engineers and researchers can benefit from this benchmarking to improve their models' performance on real-world tasks, while developers can use this information to choose the best LLM for their applications
Key Insight
💡 Local LLMs can have significantly different performance on agentic coding tasks, even when running on the same machine and backend
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🤖 Benchmarking local LLMs on agentic coding tasks reveals surprising performance gaps! 🚀
Key Takeaways
Benchmarking local LLMs on agentic coding tasks reveals surprising performance gaps between models
Full Article
I've been building QuantaMind — a desktop app for evaluating local LLMs on real agentic tasks, not just vibes. This week I ran the built-in Coding eval suite against two popular Ollama models and the gap was wider than I expected. Here's what I found. The setup Both models ran on the same machine (64 GB RAM, Workstation class), same Ollama backend, same settings: Difficulty: Easy tier Eval suite: Built-in Codin
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