Local models fail tool calling on 12GB VRAM

The "reasoning-to-VRAM" bottleneck remains the primary obstacle for local AI agents, even with the latest 2026 model releases.

• –12GB of VRAM is the "awkward middle": it accommodates 7B-14B models easily but forces larger, more capable models like the 26B+ range into heavy quantization that strips away tool-calling reliability.
• –Qwen 3.6-Plus-7B is currently the most robust "small" model for tool-use, yet it still suffers from "instruction drift" when a task requires multi-step repository navigation.
• –Hardware detection tools like llmfit can confirm a model "fits," but they cannot account for the massive KV cache growth required for Claude Code's extensive context-window needs.
• –The release of Gemma 4 (26B MoE) on April 2, 2026, provides a potential solution, but its performance on 12GB cards is hampered by the need for partial system RAM offloading.