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[2502.05376] LO-BCQ: Block Clustered Quantization for 4-bit (W4A4) LLM Inference
Summary
The paper presents LO-BCQ, a novel block clustered quantization method for 4-bit LLM inference, achieving less than 1% accuracy loss while optimizing storage and computational efficiency.
Why It Matters
As large language models (LLMs) become increasingly resource-intensive, efficient quantization methods like LO-BCQ are crucial for deploying these models in real-world applications. This research addresses the challenge of maintaining performance while significantly reducing resource requirements, making advanced AI more accessible.
Key Takeaways
- LO-BCQ enables effective 4-bit quantization of LLMs with minimal accuracy loss.
- The method clusters operand tensors to optimize quantization codebooks.
- Achieves state-of-the-art results in post-training quantization without additional training costs.
- Demonstrates practical implications for deploying LLMs in resource-constrained environments.
- Contributes to ongoing advancements in efficient AI model deployment.
Computer Science > Machine Learning arXiv:2502.05376 (cs) [Submitted on 7 Feb 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:LO-BCQ: Block Clustered Quantization for 4-bit (W4A4) LLM Inference Authors:Reena Elangovan, Charbel Sakr, Anand Raghunathan, Brucek Khailany View a PDF of the paper titled LO-BCQ: Block Clustered Quantization for 4-bit (W4A4) LLM Inference, by Reena Elangovan and 3 other authors View PDF HTML (experimental) Abstract:Post-training quantization (PTQ) is a promising approach to reducing the storage and computational requirements of large language models (LLMs) without additional training cost. Recent PTQ studies have primarily focused on quantizing only weights to sub-8-bits while maintaining activations at 8-bits or higher. Accurate sub-8-bit quantization for both weights and activations without relying on quantization-aware training remains a significant challenge. We propose a novel quantization method called block clustered quantization (BCQ) wherein each operand tensor is decomposed into blocks (a block is a group of contiguous scalars), blocks are clustered based on their statistics, and a dedicated optimal quantization codebook is designed for each cluster. As a specific embodiment of this approach, we propose a PTQ algorithm called Locally-Optimal BCQ (LO-BCQ) that iterates between the steps of block clustering and codebook design to greedily minimize the quantization mean squared error. When weight and activation scalars are enc...
