Does Quantization Break Tool-Calling? I Measured It on a 4GB Laptop GPU (BFCL, 3 Seeds, Bootstrap 95% CI)
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Quantization impact on tool-calling with rigorous benchmarks is highly relevant and actionable.
Summary
QuantCall benchmarks on an RTX 3050 laptop (4GB VRAM) reveal model family dominates size for quantization robustness in tool-calling: Qwen3-0.6B retains schema-validity and argument correctness through Q4_K_M, while Llama-3.2-1B's schema-validity degrades even at Q8_0 (0.327→0.280 fp16→Q4_K_M) and its collapse is 5× larger on parallel/tool-catalog tasks. Negative results: constrained decoding (GBNF) did not improve accuracy and cost 6–86% more wall time; transformers vs llama-cpp backends showed no statistical difference at matching precision.