Source URL: https://simonwillison.net/2024/Aug/30/magic-ai/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Magic AI
Feedly Summary: We have recently trained our first 100M token context model: LTM-2-mini. 100M tokens equals ~10 million lines of code or ~750 novels.
For each decoded token, LTM-2-mini’s sequence-dimension algorithm is roughly 1000x cheaper than the attention mechanism in Llama 3.1 405B for a 100M token context window.
The contrast in memory requirements is even larger — running Llama 3.1 405B with a 100M token context requires 638 H100s per user just to store a single 100M token KV cache. In contrast, LTM requires a small fraction of a single H100’s HBM per user for the same context.— Magic AI
Tags: llms, ai, generative-ai
AI Summary and Description: Yes
Summary: The text discusses the advancements in AI model training, particularly focusing on the capabilities and efficiency of the newly developed LTM-2-mini compared to existing models like Llama 3.1. This insight is crucial for professionals in AI and generative AI security as it touches upon the cost efficiency and resource management associated with large language models (LLMs).
Detailed Description:
The text highlights the innovative approach of training a model called LTM-2-mini, which leverages a 100M token context. This development brings significant improvements, especially in terms of costs and memory requirements compared to existing LLMs like Llama 3.1. Below are the key points that capture the significance of this advancement:
– **Model Efficiency**:
– LTM-2-mini is trained on 100 million tokens, which is comparable to massive datasets (e.g., ~10 million lines of code or ~750 novels).
– The sequence-dimension algorithm utilized by LTM-2-mini is approximately 1000 times less expensive than the attention mechanism currently used in Llama 3.1 with 405 billion parameters. This significant reduction in computational cost is a compelling argument for its adoption and further development.
– **Memory Requirements**:
– Llama 3.1 necessitates 638 H100 GPUs to manage a 100M token key-value (KV) cache for a single user, which highlights the intense resource consumption typically associated with large models.
– In stark contrast, LTM-2-mini requires a minuscule fraction of a single H100’s high bandwidth memory (HBM) for equivalent processing. This not only reduces the operating costs but also potentially broadens accessibility for various applications in AI.
– **Implications for Security and Infrastructure**:
– The advancements in efficiency could lead to a shift in how resources are allocated for AI workloads, impacting cloud computing infrastructures and related security measures.
– As models become more lightweight yet powerful, organizations may find it easier to scale applications while maintaining compliance with security protocols.
In summary, the introduction of LTM-2-mini could significantly affect the landscape of AI development and deployment, particularly from an efficiency and resource management perspective, resonating strongly with security and compliance professionals aiming for cost-effective model deployments.