The AI training platform providing everything but AI!

AIchor allows LLM fine tuning and model training at scale while abstracting all the infrastructure and automating what is not AI related. AIchor platform is working either on-premise or in the cloud.

AIchor platform dashboard that shows many experiments with different steps, status, and costs.

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Why AIchor

AIchor's platform objective is to take advantage of the full power of high density CPU cores and advanced GPUs to distribute Machine Learning and Reinforcement Learning workloads at scale while abstracting the hardware and technical intricacies to AI Engineers and researchers.

Optimize the cost of infrastructure by leveraging our platform

Schedule model training and LLM fine tuning experiments

Rely on a sturdy solution with higher dataset durability

Have a seamless user experience and empower AI engineers

Seamless Distribution

A secure platform where Compute requirements specifications can be easily bootstrapped in a single file. Having one centralized access to the clusters.

Seamless Distribution A secure platform where compute requirements specifications can be easily bootstrapped in a single file, providing centralized access to clusters. Streamlining the distribution of computing resources for efficient management.

How It Works

Get your workloads running on distributed resources in 4 steps

1 Provision your infrastructure

Import or create an engine from AIchor web interface where your workload will be running.

2 Plug your VCS

Connect your repository (Github, GitLab or Bitbucket) to integrate with AIchor.

3 Trigger your job

Request your resources and simply run your job.

4 Monitor your job

Follow your job's progress and monitor its logs, resources and costs.

Workload Management

Logs management

Realtime logs being streamed and accessible via the UI

Logs management AIchor

Resources

Users can monitor and manage their workloads from a web page, no need for ssh access. They can track compute costs easily by teams or projects from the user interface.

Resources management AIchor

Storage

Ability to interact with input and output datasets.

Storage AIchor

Working with big pre-trained models is challenging in terms of hardware requirements as we are all familiar with the CUDA OUT OF MEMORY error. We found AIchor to be the perfect solution that not only fixed all of that but also optimized the cost of infrastructure by using a containerized environment and engaging the A100s only when called. Thanks to AIchor, we now only need a data scientist with docker skills in order for us to iterate and improve our models.

Chehir Dhaouadi

Co-Founder of Reedz

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