Join the next generation
in Machine Learning.
neurothink™ Beta is now accepting public submissions.
A low friction process focused on making robust models
A low friction process focused on time to efficient model delivery.
Our own dedicated industry leading GPU servers providing efficient and low-cost compute
Highly efficient and low-cost access to industry leading GPU compute.
End-to-end API-based security delivered on our own secure hardware
End-to-end API based security.
Partners that have unique IP to deliver ML models to the edge
Partners that have unique IP to deliver ML models efficiently to the edge and across a number of chip platforms (GPU, CPU, ARM, RISC V and FPGA.)
Creating a community of ML enthusiasts with a platform for networking and knowledge sharing
Creating a community of ML enthusiasts who have access to a knowledge platform and network to share, learn and host events.
Serious Machine Learning compute power
Available GPU peak capability 4 petaflops (10^15) mixed-precision & deep learning, and 489 teraflops FP32 single precision.
CY2021 statistics for neurothink?
- Available GPU peak capability 489 teraflops FP32 single precision, and 3.9 petaflops (10^15) mixed-precision and deep learning
- Greater than 170K CUDA cores
- Available RAM 12.512 TB
- All flash on-site storage array greater than 250TB
- GPUs available A100, V100 and T4
- Co-location facility, Energy Star rated, Tier III uptime certified, redundant power, and active cooled cages
How do we do it?
- Dedicated GPU compute resource (Tesla, Volta and Amper generation) in a modern tier III certified colocation facility.
- Latest server virtualization, container monitoring and GPU management software from NVIDIA and VMWare.
- Machine Learning instances are customized at the CUDA level for maximum GPU efficiency.
- A user interface that does not require switching between confusing environments.
- Work in a notebook and have access to a command line interface.
- Integrated storage environment to upload, attach, save and push the
objects you need to do your work. - Resume work on your development instance with auto-save.
Steps in the model development process
- Work starts in a secure and containerized instance. We will guide you as much as you need or give you editing power during any of the steps.
- Analyze the results, rerun the instances, test the results, or compare many instances at once to create a model.
- Complete a risk review, audit your models, and be able to explain and create documentation.
- Prepare your model for a variety of edge applications for various compute devices.
Partners
Installed Libraries & Frameworks
+ additional libraries supported thru PIP install in your container
