OSC: Hardware Efficient W4A4 Quantization via Outlier Separation in Channel Dimension

Researchers present OSC, a hardware-efficient framework that addresses the challenge of deploying Large Language Models with 4-bit quantization by intelligently separating activation outliers into a high-precision processing path while maintaining low-precision computation for standard values. The technique achieves 1.78x speedup over standard 8-bit approaches while limiting accuracy degradation to under 2.2% on state-of-the-art models.

The quantization of large language models to 4-bit precision represents a critical engineering challenge for deploying AI systems at scale. While 4-bit formats dramatically reduce memory requirements and computational overhead, they struggle with activation outliers—extreme values that exceed the constrained dynamic range of low-bit representations. OSC addresses this fundamental limitation through a dual-path architecture that recognizes a key empirical finding: outliers consistently cluster in specific channels across different input tokens, enabling predictable and efficient handling.

The research builds upon years of quantization research that has progressively reduced model precision from 32-bit floating point to 8-bit and now 4-bit formats. Each reduction creates multiplicative efficiency gains, but outlier handling has remained a bottleneck. Previous approaches either applied uniform high precision across all channels or attempted to mask outliers, both reducing throughput gains. OSC's innovation lies in offline identification of outlier-prone channels through group-wise analysis, then dynamically routing only these channels through a high-precision 16-bit path during inference.

For deployment infrastructure, this approach aligns perfectly with modern AI accelerator hardware designed for 4-bit operations, avoiding custom logic or significant architectural modifications. The 1.78x speedup over W8A8 baselines demonstrates that hardware efficiency translates directly to production value. Testing on Qwen models shows accuracy preservation comparable to higher-precision alternatives, making OSC immediately viable for commercial deployment.

The integration of fallback strategies for W2 quantization scenarios shows practical engineering maturity. Future work will likely explore extending this channel-clustering insight to other model architectures and investigating whether outlier patterns vary meaningfully across different domains or fine-tuned variants.

• →OSC uses offline channel analysis to identify outlier locations, enabling efficient online separation without dynamic overhead
• →Dual-path architecture routes 4-bit general operations and 16-bit outlier operations to match modern hardware capabilities
• →Achieves 1.78x speedup over W8A8 baseline while maintaining under 2.2% accuracy degradation on 8B and 30B parameter models
• →Token-persistent outlier clustering in fixed channels is the key empirical finding enabling the structured separation approach
• →Framework integrates FP8 fallback strategy for lower-precision quantization scenarios with weaker outlier patterns