24GB VRAM dual-GPU setup powers local 32B models

24GB of VRAM is the sweet spot for high-end local coding assistants — it is enough to run 32B models without losing significant quality to heavy quantization.

• –Qwen2.5-Coder 32B is the strongest generalist for Python, Solidity, and Rust due to its expansive training data and Fill-in-the-Middle (FIM) support
• –DeepSeek-V3.2's Chain-of-Thought reasoning is essential for auditing complex blockchain smart contracts where logic errors are costly
• –The dual-GPU setup (16GB + 8GB) allows splitting models across both cards, though keeping a 16B model entirely on the 5060 Ti will minimize PCIe latency
• –Increasing the context window to 16k or 32k is critical for multi-file blockchain projects including parsers, RPC wrappers, and test suites
• –DeepSeek-Coder-V2 Lite 16B provides a "Tab-Autocomplete" speed alternative that only uses ~10GB of VRAM