A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models

2026-04-15 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, quick

Summary

A new framework addresses the challenge of deploying large language models (LLMs) on edge devices by proposing a lightweight, backpropagation-free sensitivity analysis for hybrid Structured State Space Model (SSM)-Transformer architectures. This method identifies model components most vulnerable to quantization-induced performance degradation using only forward-pass metrics, eliminating the need for expensive gradient computations or retraining. The framework formally demonstrates that Kullback-Leibler (KL) divergence is a superior metric for capturing quantization sensitivity in language modeling tasks compared to mean squared error (MSE) and signal-to-quantization-noise ratio (SQNR). Validated on Intel Lunar Lake hardware, KL-guided mixed-precision quantization achieves near-FP16 perplexity while maintaining competitive model sizes and throughput with Uniform INT4 across both CPU and GPU.

Key takeaway

For NLP engineers deploying LLMs on resource-constrained edge devices, adopting this KL-based, forward-only sensitivity analysis can significantly improve mixed-precision quantization strategies. Your teams can achieve near-FP16 performance with smaller model sizes and higher throughput, making advanced hybrid SSM-Transformer models viable for on-device intelligence without extensive retraining or proprietary data access.

Key insights

KL divergence effectively identifies quantization sensitivity in hybrid SSM-Transformer models for efficient edge deployment.

Principles

• Forward-pass metrics suffice for quantization sensitivity.
• KL divergence outperforms MSE/SQNR for language modeling.
• Mixed-precision quantization optimizes edge LLM deployment.

Method

The method uses a surrogate-based, backpropagation-free sensitivity analysis, relying on forward-pass metrics and KL divergence to rank component susceptibility to quantization degradation.

In practice

• Apply KL divergence for quantization sensitivity analysis.
• Use mixed-precision for hybrid SSM-Transformer models.
• Deploy LLMs on Intel Lunar Lake with KL-guided quantization.

Topics

• Mixed-Precision Quantization
• SSM-Transformer Models
• KL Divergence
• Quantization Sensitivity
• Edge AI Deployment

Code references

• jasonkongie/kl-ssm-quant

Best for: NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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• A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models — KL divergence is a superior metric for identifying quantization sensitivity in hybrid SSM-Transformer LLMs.
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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.