VQ-Logits: Compressing the Output Bottleneck of Large Language Models via Vector Quantized Logits

TL;DR

VQ-Logits introduces a vector quantization-based method to significantly compress the output layer of large language models, reducing parameters and computation with minimal impact on performance.

Contribution

It proposes a novel VQ-based output layer that replaces large vocab embedding matrices, enabling substantial parameter reduction and faster inference in LLMs.

Findings

Achieves up to 99% parameter reduction in output layer

Provides 6x speedup in logit computation

Maintains only 4% increase in perplexity

Abstract

Large Language Models (LLMs) have achieved remarkable success but face significant computational and memory challenges, particularly due to their extensive output vocabularies. The final linear projection layer, mapping hidden states to vocabulary-sized logits, often constitutes a substantial portion of the model's parameters and computational cost during inference. Existing methods like adaptive softmax or hierarchical softmax introduce structural complexities. In this paper, we propose VQ-Logits, a novel approach that leverages Vector Quantization (VQ) to drastically reduce the parameter count and computational load of the LLM output layer. VQ-Logits replaces the large V * dmodel output embedding matrix with a small, shared codebook of K embedding vectors (K << V ). Each token in the vocabulary is mapped to one of these K codebook vectors. The LLM predicts logits over this compact codebook, which are then efficiently "scattered" to the full vocabulary space using the learned or preassigned mapping. We demonstrate through extensive experiments on standard language modeling benchmarks (e.g., WikiText-103, C4) that VQ-Logits can achieve up to 99% parameter reduction in the output layer and 6x speedup in logit computation, with only a marginal 4% increase in perplexity compared to full softmax baselines. We further provide detailed ablation studies on codebook size, initialization, and learning strategies, showcasing the robustness and effectiveness of our approach.