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Average Ratings 0 Ratings

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ease
features
design
support

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Description

Evo 2 represents a cutting-edge genomic foundation model that excels in making predictions and designing tasks related to DNA, RNA, and proteins. It employs an advanced deep learning architecture that allows for the modeling of biological sequences with single-nucleotide accuracy, achieving impressive scaling of both compute and memory resources as the context length increases. With a robust training of 40 billion parameters and a context length of 1 megabase, Evo 2 has analyzed over 9 trillion nucleotides sourced from a variety of eukaryotic and prokaryotic genomes. This extensive dataset facilitates Evo 2's ability to conduct zero-shot function predictions across various biological types, including DNA, RNA, and proteins, while also being capable of generating innovative sequences that maintain a plausible genomic structure. The model's versatility has been showcased through its effectiveness in designing operational CRISPR systems and in the identification of mutations that could lead to diseases in human genes. Furthermore, Evo 2 is available to the public on Arc's GitHub repository, and it is also incorporated into the NVIDIA BioNeMo framework, enhancing its accessibility for researchers and developers alike. Its integration into existing platforms signifies a major step forward for genomic modeling and analysis.

Description

Persistence container technology facilitates efficient operations with a lightweight approach, allowing users to pay for usage by the second instead of waiting for hours or months. The payment process, which will occur via credit card, is set for the following month. This technology offers high performance at a competitive price compared to alternative solutions. Furthermore, it is set to be deployed in the fastest supercomputer globally at Oak Ridge National Laboratory. Various machine learning applications, including deep learning, computational fluid dynamics, video encoding, 3D graphics workstations, 3D rendering, visual effects, computational finance, seismic analysis, molecular modeling, and genomics, will benefit from this technology, along with other GPU workloads in server environments. The versatility of these applications demonstrates the broad impact of persistence container technology across different scientific and computational fields.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Evo Designer Yes 
GitHub Yes 
Hugging Face Yes 
Keras No 
NVIDIA BioNeMo Yes 
PyTorch No 
TensorFlow No 

Integrations

Evo Designer No 
GitHub No 
Hugging Face No 
Keras Yes 
NVIDIA BioNeMo No 
PyTorch Yes 
TensorFlow Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$0.0992 per hour
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Arc Institute

Country

United States

Website

arcinstitute.org/tools/evo

Vendor Details

Company Name

GPUEater

Country

United States

Website

gpueater.com

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