ETH Zurich researchers developed an approach using Fast Feedforward Networks (FFF) to increase the speed of Large Language Models (LLM). By engaging only a small fraction of neurons for individual inferences, their UltraFastBERT model could potentially run 341x faster, although a software workaround currently yields a 78x improvement.
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Unlock Potential: AI Solutions for Efficient Business Management
Exciting news from ETH Zurich: Researchers have developed a way to drastically speed up Large Language Models (LLMs) like GPT and BERT. This breakthrough could mean a leap in AI performance and efficiency for businesses.
Understanding the Breakthrough
Traditional AI models perform complex calculations with all neurons at each layer. However, the new Fast Feedforward Networks (FFF) approach selects only essential neurons, like sorting mail by zip code to find relevant letters quickly. This method reduces processing demands substantially.
Practical Implications
Imagine an AI model, UltraFastBERT, which uses just 0.03% of its neurons for processing, leading to potential speed improvements up to 341x compared to traditional methods. Though in real-world applications, a 78x speed increase has been achieved due to software limitations.
The catch? Current advancements in dense matrix multiplication are kept under wraps by companies like Intel and Nvidia, limiting the full potential of FFF networks. If these secrets were to be shared, the theoretical speed gains could become a reality, transforming AI efficiency.
Embrace AI for Business Growth
To stay competitive, consider how the new, faster AI models can elevate your company. Look for automation opportunities, set clear KPIs, choose the right AI solutions, and implement them step by step. For expert advice, contact us at hello@itinai.com and follow our updates on Telegram t.me/itinainews and Twitter @itinaicom.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- New approach could make large language models 300x faster
- DailyAI
- Twitter – @itinaicom
