Choosing a Local LLM for Three.js Development: A Practical Quality vs. Speed Tradeoff
An AI developer compared two open-source models on an RTX 3060 for coding tasks, revealing that a slower, larger model can deliver more accurate results for specific workflows.
Practical Summary
A developer tested Google's Gemma 4 12B QAT and Alibaba's Qwen 3.6 35B-A3B for generating Three.js code on an RTX 3060 12GB GPU. Despite Gemma being twice as fast and fitting entirely in VRAM, the larger Qwen model consistently produced more accurate implementations for the test prompts. The developer chose Qwen for better quality, accepting its speed and memory tradeoffs. This provides a real-world example of evaluating local LLMs for a specific coding workflow.
Why It Matters
This case study shows that for specialized tasks like 3D game development with Three.js, raw inference speed and memory efficiency aren't the only factors. The quality and accuracy of the generated code, relative to the prompt, are critical for productivity. Developers optimizing local LLM workflows need to benchmark models on their specific use cases, as general performance metrics may not predict task-specific results.
How to Evaluate Local LLMs for Your Coding Workflow
Start by identifying a specific, representative task for your workflow. Here, the developer chose generating Three.js simulation code from prompts. Use a small set of consistent test prompts that cover your typical needs.
Set up a controlled test environment. The developer used llama.cpp with consistent settings: MTP enabled, Q8 KV cache, and same inference parameters for both models. Ensure other system conditions are identical to isolate model performance.
Run the models on your target hardware. Note the key metrics: tokens per second (speed) and whether the model fits in VRAM or requires offloading. Here, Gemma 4 12B fit in 12GB VRAM at ~50 tok/s, while Qwen 3.6 35B-A3B required offloading at ~25-30 tok/s.
Evaluate the output quality against your prompts. Don't rely solely on speed. The developer found that despite being slower, Qwen's generated Three.js code was consistently more accurate to the prompt's requirements. This subjective but critical quality check often matters more than raw throughput for coding tasks.
Make a tradeoff decision based on your priorities. For this developer, output accuracy for Three.js development outweighed the speed and memory advantages of the smaller model. Document your choice and the reasons for future reference as models and tools evolve.