Enterprise AI Analysis
SkipCat: Rank-Maximized Low-Rank Compression of Large Language Models via Shared Projection and Block Skipping
Authors: Yu-Chen Lu et al.
Publication: arXiv:2512.13494v1 [cs.CL] 15 Dec 2025
SkipCat introduces an innovative low-rank compression framework for Large Language Models (LLMs) that significantly enhances efficiency without compromising performance. It achieves this through two core techniques: shared low-rank projections (Cat) and block skipping (Skip). Cat allows multiple matrices within a layer to share a common projection, reducing redundancy. Skip further optimizes by omitting computations for certain sub-blocks in the decomposition, enabling higher effective ranks under the same compression budget. Experimental results show SkipCat outperforms existing low-rank methods, yielding up to 7% accuracy improvement on zero-shot tasks and better perplexity, even at aggressive compression rates, making LLMs more feasible for edge device deployment.
Executive Impact: Why SkipCat Matters for Your Business
SkipCat's approach to LLM compression offers significant tangible benefits for enterprises, enabling more efficient and cost-effective AI deployments, especially on edge devices or in resource-constrained environments.
Accuracy Improvement (Zero-Shot Tasks)
Perplexity Reduction (30% Compression)
Decoding Speedup (80% Compression, LLaMA2-7B)
Deep Analysis & Enterprise Applications
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Model compression is crucial for deploying LLMs on resource-constrained edge devices. Traditional low-rank methods often require aggressive rank reduction, leading to performance degradation. SkipCat addresses this trade-off by maximizing effective ranks under a given compression budget.
7%
Accuracy Improvement over SVD-LLM on Zero-Shot Tasks at 30% Compression
SkipCat Methodology Flow
Original Dense Layer
→
Naïve Low-Rank (SVD)
→
Cat Low-Rank Layer (Shared Projection)
→
SkipCat Low-Rank Layer (Block Skipping)
| Feature | Naïve Low-Rank (SVD) | SkipCat (Ours) |
|---|---|---|
| Rank Retention | Requires significant reduction for efficiency | Maximizes effective ranks under same budget |
| Compression Efficiency | Effective only below 50% full rank | Effective across wider range, higher ranks |
| Numerical Stability | Prone to FP16 overflow issues | Stabilized with column permutation |
| Applicability | General SVD factorization | Intra-layer shared projection + block skipping |
LLaMA2-7B Deployment Enhancement
SkipCat demonstrated significant benefits for LLaMA2-7B. At a 30% compression rate, it achieved 7% higher accuracy on zero-shot tasks compared to existing low-rank methods, and only a 1.4x increase in perplexity on WikiText-2 (versus 2.1x for other methods). This enables robust deployment of LLaMA2-7B on edge devices without compromising performance.
Zero-shot Accuracy (30% Comp) Baseline: 33.12%
WikiText-2 Perplexity (30% Comp) Baseline: 208.55
Deploying LLMs on edge devices is challenging due to their large size and computational demands. SkipCat addresses these by reducing both FLOPs and memory footprint, making LLMs more suitable for resource-constrained environments.
3.26x
Decoding Speedup at 80% Compression
SkipCat for Edge Deployment
High Parameter Count LLM
→
Apply Cat (Shared Projection)
→
Apply Skip (Block Skipping)
→
Optimized for Edge Inference
| Metric | Naïve Low-Rank (r < R/2) | SkipCat (Ours) |
|---|---|---|
| FLOPs Reduction Threshold | Below 50% of full rank | Effective across wider rank range |
| Memory Reduction Threshold | Below 50% of full rank | Effective across wider rank range |
| Performance Preservation | Significant degradation | Minimizes degradation at high compression |
| Hardware Compatibility | General matrix operations | Optimized for FP16 inference stability |
Quantify Your Enterprise AI Savings
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Your Path to Optimized LLMs
A structured approach to integrate SkipCat's advanced compression into your enterprise AI strategy.
Phase 1: Assessment & Strategy
Evaluate current LLM usage, identify optimization targets, and define performance benchmarks. Our experts will help you scope the project and establish success criteria.
Phase 2: Model Integration & Tuning
Implement SkipCat compression on your chosen LLMs. This phase includes initial training-free compression, followed by optional LoRA fine-tuning for domain-specific adaptation, ensuring optimal performance.
Phase 3: Deployment & Monitoring
Deploy the compressed models on target edge devices or inference infrastructure. Establish robust monitoring to track performance, efficiency, and resource utilization in production.
Phase 4: Scaling & Continuous Improvement
Expand SkipCat's application to additional LLMs and use cases. Continuously optimize models based on ongoing performance data and evolving business needs.
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