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ARXIV:2604.13440 · LLM OPTIMIZATION · SUBMITTED 16 APR · 18:18 UTC · FRESHNESS STALE
ARXIV:2604.13440LLM OPTIMIZATIONSUBMITTED 16 APR · 18:18 UTCFRESHNESS STALEJason Kong · Nilesh Prasad Pandey · Flavio Ponzina · Tajana Rosing · arXiv
A fast, forward-only sensitivity analysis using KL divergence for mixed-precision SSM-Transformer models, enabling efficient LLM deployment on edge devices.
Opportunity summary
Pain A fast, forward-only sensitivity analysis using KL divergence for mixed-precision SSM-Transformer models, enabling efficient LLM deployment on edge devices.
Evidence 0 refs | 4 sources | 50% coverage
Blocker Evidence unverified
A fast, forward-only sensitivity analysis using KL divergence for mixed-precision SSM-Transformer models, enabling efficient LLM deployment on edge devices. Hybrid architectures combining Structured State Space Models (SSMs) with transformer-based LLMs offer a balance of…
Deploying Large Language Models (LLMs) on edge devices faces severe computational and memory constraints, limiting real-time processing and on-device intelligence. Hybrid architectures combining Structured State Space Models (SSMs) with transformer-based LLMs offer a balance…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. This framework enables the practical deployment of advanced hybrid models on resource-constrained edge devices with minimal accuracy loss. A public repository is linked, so…
LLM Optimization moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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A fast, forward-only sensitivity analysis using KL divergence for mixed-precision SSM-Transformer models, enabling efficient LLM deployment on edge devices.
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10.48550/arXiv.2604.13440A fast, forward-only sensitivity analysis using KL divergence for mixed-precision SSM-Transformer models, enabling efficient LLM deployment on edge devices.
Abstract
Deploying Large Language Models (LLMs) on edge devices faces severe computational and memory constraints, limiting real-time processing and on-device intelligence. Hybrid architectures combining Structured State Space Models (SSMs) with transformer-based LLMs offer a balance of efficiency and performance. Aggressive quantization can drastically cut model size and speed up inference, but its uneven effects on different components require careful management. In this work, we propose a lightweight, backpropagation-free, surrogate-based sensitivity analysis framework to identify hybrid SSM-Transformer components most susceptible to quantization-induced degradation. Relying solely on forward-pass metrics, our method avoids expensive gradient computations and retraining, making it suitable for situations where access to in-domain data is limited due to proprietary restrictions or privacy constraints. We also provide a formal analysis showing that the Kullback-Leibler (KL) divergence metric better captures quantization sensitivity for Language modeling tasks than widely adopted alternatives such as mean squared error (MSE) and signal-to-quantization-noise ratio (SQNR). Through extensive experiments on SSM and hybrid architectures, our ablation studies confirm that KL-based rankings align with observed performance drops and outperform alternative metrics. This framework enables the practical deployment of advanced hybrid models on resource-constrained edge devices with minimal accuracy loss. We further validate our approach with real-world on-device profiling on Intel Lunar Lake hardware, demonstrating that KL-guided mixed-precision achieves near-FP16 perplexity with model sizes and throughput competitive with Uniform INT4 on both CPU and GPU execution modes. Code is available at https://github.com/jasonkongie/kl-ssm-quant.
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PROBLEM
A fast, forward-only sensitivity analysis using KL divergence for mixed-precision SSM-Transformer models, enabling efficient LLM deployment on edge devices. Hybrid architectures combining Structured State Space Models (SSMs) with transformer-based LLMs offer a balance of efficie...
METHOD
Deploying Large Language Models (LLMs) on edge devices faces severe computational and memory constraints, limiting real-time processing and on-device intelligence. Hybrid architectures combining Structured State Space Models (SSMs) with transformer-based LLMs offer a balance of...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. This framework enables the practical deployment of advanced hybrid models on resource-constrained edge devices with minimal accuracy loss. A public repository is linked, so build verification can inspect...
WHY NOW
LLM Optimization moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A fast, forward-only sensitivity analysis using KL divergence for mixed-precision SSM-Transformer models, enabling efficient LLM deployment on edge devices. Hybrid architectures combining Structured State Space Models (SSMs) with transformer-based LLMs offer a balance of efficiency and performance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Deploying Large Language Models (LLMs) on edge devices faces severe computational and memory constraints, limiting real-time processing and on-device intelligence. Hybrid architectures combining Structured State Space Models (SSMs) with transformer-based LLMs offer a balance of efficiency and performance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. This framework enables the practical deployment of advanced hybrid models on resource-constrained edge devices with minimal accuracy loss. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Optimization moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A fast, forward-only sensitivity analysis using KL divergence for mixed-precision SSM-Transformer models, enabling efficient LLM deployment on edge devices.
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LLM Optimization
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8.0/10 public viability
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