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Embedding table precision is key for LLM size reduction

By PulseAugur Editorial · [1 sources] ·

A recent experiment explored the impact of quantization on Transformer models, revealing that the embedding table constitutes a significant portion (72%) of the model's parameters. The research found that quantizing the embedding table independently, particularly with per-row scales, offers substantial byte reduction with minimal loss in performance. This approach allows for a 6.2x reduction in model size while maintaining or even improving system-level performance by reallocating freed-up bytes to the count table and cache. AI

IMPACT Optimizing embedding table quantization can lead to significantly smaller models without sacrificing performance, enabling wider deployment.

RANK_REASON The item details findings from an experiment on model quantization and parameter distribution, which is a research-level contribution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Embedding table precision is key for LLM size reduction

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35 / 100
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The item details findings from an experiment on model quantization and parameter distribution, which is a research-level contribution. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Seth Wheeler ·

    The Embedding Table Was 72% of the Model

    <p>An earlier experiment in this series had established something slightly deflating about a small transformer: quantising the whole network to int8 is free, and the bytes you save are better spent on count tables than on network precision. That is a useful result and it invites …

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