Peter Jang
May 5, 2025 22:08
Arcee AI is migrated to a dedicated endpoint with AWS to optimize the cost and performance of special compact language models to improve operating agility and efficiency.
Archee AI, a company that focuses on simplifying AI adoption, strategically moved by switching a specialized small language model (AWS) from Amazon Web Services (AWS) to a dedicated endpoint. This migration has greatly improved the cost efficiency, performance and operating agility of Archee AI, according to Together.ai.
Small language model optimization
The core of the strategy of ARCEE AI is the development of a special compact language model optimized for specific tasks of less than 72 billion parameters. The company uses monopoly technology for model education, merger and distillation to create excellent high -performance models in tasks such as coding, text creation and high -speed reasoning.
With the migration to AI, seven of these models can now access the serverless endpoint of the AI. This model includes Arcee AI Virtuoso-Large, Arcee AI Virtuoso-Medium, Arcee AI MAESTRO, Arcee AI CODER-LARGE Blitz is included, and it is designed for various complex tasks, from coding to visual tasks.
Software Improvement: Arcee Districtor & Arcee Orchestra
Arcee AI also developed two software products: Arcee Districtor and Arcee Orchestra to improve AI products. The conductor acts as an intelligent reasoning routing system and indicates the query efficiently as the best model according to the work requirements. This system not only reduces costs, but also uses the most suitable model for each task to improve performance benchmarks.
Arcee Orchestra focuses on building agent workflow so that companies can fully integrate with third -party services to automate their tasks. The No-Code interface makes it easy to create an automated workflow that runs with AI-centric functions.
Challenge to move to AI with AWS
Initially, ARCEE AI distributed the model through EKS, AWS’s managed Kubernetes service. However, this setting requires important engineering resources and expertise, so we have raised a troubled and costly task. AWS’s GPU prices and procurement issues urge Archee AI to find alternative solutions due to more complex problems.
The dedicated endpoint provides a managed GPU distribution and does not require in -house infrastructure management. This transition is simplified by simplifying the operation of ARCEE AI to increase flexibility and cost efficiency. The migration process was smooth as the AI managed the infrastructure and provided API access to the ARCEE AI model.
Prospect of performance and future
After migration, the ARCEE AI reported the performance improvement of the entire model, achieving more than 41 queries per second and significantly reducing the waiting time. These improvements have been deployed to continue to expand and innovate the product within the landscape of AI.
In the future, Archee AI plans to integrate the model with the Arcee Orchestra and improve the arcee conductor in a professional mode for tool code and coding. Together, AI is trying to ensure excellent performance and cost efficiency by optimizing the infrastructure to support the growth of ARCEE AI.
This partnership reflects the evolutionary epidemiology of the AI industry, which uses cloud -based solutions to improve the product and provide better investment revenue. Please visit together for more information.
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