FAMPWQ: Fisher Information-based Adaptive Mixed Precision Weight Quantization for Effective LLM Inference
FAMPWQ introduces a Fisher information-based metric to measure layer-wise sensitivity to quantization in LLMs A reinforcement learning-based bit-width allocator dynamically assigns adaptive precision per layer Outperforms 7 baselines across 7 models and 5 benchmarks with up to 3.39 lower PPL, 6.87% higher accuracy, and 76% win rate in LLM-as-a-judge evaluations Enables effective LLM inference on commodity GPUs by avoiding the performance degradation of uniform bit-width quantization
Analysis
TL;DR
- FAMPWQ introduces a Fisher information-based metric to measure layer-wise sensitivity to quantization in LLMs
- A reinforcement learning-based bit-width allocator dynamically assigns adaptive precision per layer
- Outperforms 7 baselines across 7 models and 5 benchmarks with up to 3.39 lower PPL, 6.87% higher accuracy, and 76% win rate in LLM-as-a-judge evaluations
- Enables effective LLM inference on commodity GPUs by avoiding the performance degradation of uniform bit-width quantization
Why It Matters
This work addresses a critical bottleneck in LLM deployment—balancing inference efficiency with model quality on resource-constrained hardware. By combining Fisher information theory with reinforcement learning for bit-width allocation, it offers a principled alternative to heuristic-based quantization strategies that have dominated the field.
Technical Details
- Proposes a system model using Fisher information as a novel sensitivity metric to quantify how much each layer's weights contribute to output distribution shifts under quantization
- Designs a reinforcement learning agent that takes Fisher information scores as input and outputs an adaptive mixed-precision bit-width allocation strategy across layers
- Evaluates on 7 LLMs and 5 benchmarks, comparing against 7 baseline quantization approaches
- Metrics include perplexity (PPL), task accuracy, and LLM-as-a-judge pairwise comparisons
Industry Insight
- Fisher information provides a theoretically grounded, layer-aware sensitivity signal that could become a standard tool for quantization-aware model optimization
- The RL-based allocator demonstrates that automated search over bit-width configurations can outperform hand-tuned or greedy heuristics, suggesting broader applicability to other compression techniques
- As commodity GPU deployment of LLMs becomes increasingly competitive, mixed-precision strategies that preserve accuracy while reducing compute will be essential for production efficiency
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