GuidedQuant: Large language model quantization via exploiting end loss guidance

Analysis

Why This Paper Matters

Post-training quantization (PTQ) is essential for deploying large language models (LLMs) in production, as it reduces memory footprint and latency without costly retraining. However, existing PTQ methods often suffer from accuracy loss because they optimize quantization parameters using proxy objectives (e.g., layer-wise reconstruction error) that do not directly align with the final task loss. GuidedQuant addresses this gap by introducing end-loss guidance, which directly minimizes the impact of quantization on the model's end-to-end performance. This is particularly important as LLMs grow to hundreds of billions of parameters, making even small accuracy improvements valuable.

Technical Contributions

• End-loss guidance: The key innovation is a differentiable quantization scheme that propagates gradients from the final task loss back to the quantization parameters (scale and zero-point), enabling direct optimization.
• Lightweight optimization: Unlike quantization-aware training (QAT), GuidedQuant requires only a small calibration set (e.g., 128 samples) and a few gradient steps, making it practical for large models.
• Broad applicability: The method is evaluated on multiple LLM families (LLaMA, OPT, GPT) and various bit-widths (4-bit, 8-bit), showing consistent improvements.
• No architectural changes: GuidedQuant works with standard quantization formats (e.g., symmetric, asymmetric) and does not modify the model architecture.

Results

On the LLaMA-7B model, GuidedQuant achieves a perplexity of 5.8 on WikiText-2 at 4-bit weight quantization, compared to 6.2 for the best baseline (GPTQ). For OPT-13B, the method reduces perplexity degradation by 25% relative to prior PTQ methods. On downstream tasks (e.g., HellaSwag, WinoGrande), GuidedQuant improves accuracy by 1-2 percentage points over GPTQ and Round-To-Nearest baselines. The optimization adds only 0.1% overhead to the quantization process.

Significance

GuidedQuant provides a practical bridge between fast PTQ and more expensive QAT, offering a new trade-off point that prioritizes accuracy without sacrificing deployment speed. This work could influence future quantization frameworks to incorporate task-level objectives, potentially enabling LLMs to run on consumer GPUs and mobile devices with minimal accuracy loss. The approach also opens avenues for adaptive quantization that adjusts to specific downstream tasks.