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What is LTM-2-mini?

LTM-2-mini is designed to manage a context of 100 million tokens, which is roughly equivalent to about 10 million lines of code or approximately 750 full-length novels. This model utilizes a sequence-dimension algorithm that proves to be around 1000 times more economical per decoded token compared to the attention mechanism employed by Llama 3.1 405B when operating within the same 100 million token context window. Additionally, the difference in memory requirements is even more pronounced; running Llama 3.1 405B with a 100 million token context requires an impressive 638 H100 GPUs per user just to sustain a single 100 million token key-value cache. In stark contrast, LTM-2-mini only needs a tiny fraction of the high-bandwidth memory available in one H100 GPU for the equivalent context, showcasing its remarkable efficiency. This significant advantage positions LTM-2-mini as an attractive choice for applications that require extensive context processing while minimizing resource usage. Moreover, the ability to efficiently handle such large contexts opens the door for innovative applications across various fields.

What is LTM-1?

Magic’s innovative LTM-1 technology enables context windows that are 50 times greater than the standard ones found in traditional transformer models. Consequently, Magic has created a Large Language Model (LLM) capable of efficiently handling extensive contextual information for generating recommendations. This breakthrough empowers our coding assistant to thoroughly examine and utilize your entire code repository. By drawing on a wealth of factual knowledge and its own previous interactions, larger context windows greatly improve the accuracy and cohesiveness of AI-generated responses. We are enthusiastic about the possibilities this research presents for enhancing user experiences in coding assistance tools, paving the way for smarter, more intuitive interactions. Ultimately, we believe these advancements will significantly transform how developers engage with their coding environments.

Media

Media

Integrations Supported

Integrations Supported

Quickwork

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Not specified

Customer Service / Support

Not specified

Training Options

Not specified

Training Options

Documentation Hub

Company Facts

Organization Name

Magic AI

Date Founded

2022

Company Location

United States

Company Website

magic.dev/

Company Facts

Organization Name

Magic AI

Date Founded

2022

Company Location

United States

Company Website

magic.dev/blog/ltm-1

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

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