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Recent advancements in AI model quantization are significantly enhancing the efficiency of large language models (LLMs) for deployment on edge devices. Key innovations include Arbitrary-bit quantization (ABQ) and AQLM + PV-Tuning, which offer substantial memory compression gains. ABQ achieves a 4.8X memory compression over FP16, while AQLM + PV-Tuning compresses the Llama 13B model to an effective size of 6.9B. These developments are crucial for deploying high-performance AI models in resource-constrained environments. Additionally, a collaboration between Google DeepMind, Intel, and Georgia Tech has introduced a multiplication-less LLM training method, reducing memory usage by over 80% for models like OPT-66B and LLaMA-2-70B.
Wild Paper from @GoogleDeepMind, @Intel and @GeorgiaTech Multiplication-Less LLM training - Reduces memory usage by over 80% for OPT-66B and LLaMA-2-70B models. The Problem 🔍: Existing LLM efficiency methods still rely on costly multiplications, and reparameterization… https://t.co/SaBsr6dplp
This development not only pushes the boundaries of what's possible with model compression but also opens up new avenues for deploying high-performance AI models in resource-constrained environments. #Sentients #AI #LLAMA #optimization #LLMs #MachineLearning @nvidia @NVIDIAAIDev https://t.co/v0h0k7C0S9
A few years ago, running large models like Llama3 70B on an RTX 3090 was just a dream. Today, it's a reality! Introducing AQLM + PV-Tuning: the best algorithm for 2-bit quantization yet in terms of quality It compresses the Llama 13B model down to an effective size of 6.9B https://t.co/bNmxWOFVlq
