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Description
Everpure FlashBlade//S is a high-performance scale-out storage solution designed for enterprise file and object data. It helps organizations manage rapidly growing unstructured data with a unified platform that supports NFS, SMB, and S3 access. Built on DirectFlash technology and the Purity operating system, FlashBlade//S delivers fast, efficient, and reliable storage without relying on legacy disk-based or hybrid architectures. The platform is designed for demanding workloads such as AI, machine learning, RAG, training, inference, simulation, genomics, analytics, HPC, EDA, financial services, healthcare, data protection, and rapid restore. Its modular architecture lets teams scale compute and storage independently, helping them match capacity and performance to changing workload needs. FlashBlade//S can support billions of files and objects in a single system while providing centralized management through Pure1. The solution also includes enterprise data services such as compression, global erasure coding, always-on encryption, SafeMode, file replication, and object replication. Capabilities such as Zero Move Tiering and Rapid Replicas help improve access to hot and cold data, reduce bandwidth consumption, and support collaboration across distributed teams. With Evergreen subscriptions, next-generation R2 blades, nondisruptive upgrades, and improved energy efficiency, FlashBlade//S gives enterprises a future-ready platform for simplifying and scaling unstructured data storage.
Description
The Ling 2.6 Flash represents the newest and most economical addition to the Ling series, utilizing a Mixture of Experts architecture that encompasses a total of 104 billion parameters, with 7.4 billion of those being actively engaged. This model is crafted to strike an ideal balance between inference speed and computational expense, making it an excellent fit for diverse scenarios where reasoning prowess, high throughput, and effective deployment are essential. By employing its MoE structure, Ling ensures that each token activates only the most pertinent expert subnetworks, significantly reducing the actual computational load while preserving the expansive capacity of the model. Offering a native context window of 256K, Ling 2.6 Flash is capable of handling around 200,000 characters of lengthy input, adeptly retrieving critical long-range information regardless of its position in the context. Furthermore, its overall benchmark performance rivals or surpasses that of 40 billion parameter Dense models, highlighting its competitive edge in the field of AI. This blend of efficiency and performance makes Ling 2.6 Flash a noteworthy option for developers seeking advanced capabilities without excessive resource demands.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
AWS Cloud9
Yes
Actifio
Yes
Care Director
Yes
Cisco Adaptive Security Appliance (ASA)
Yes
Cloudera
Yes
Commvault HyperScale X
Yes
Dizzion
Yes
Everpure
Yes
INAP DRaaS
Yes
JFrog Insight
Yes
Integrations
AWS Cloud9
No
Actifio
No
Care Director
No
Cisco Adaptive Security Appliance (ASA)
No
Cloudera
No
Commvault HyperScale X
No
Dizzion
No
Everpure
No
INAP DRaaS
No
JFrog Insight
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
$0.00037 per 1M tokens
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
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
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Everpure
Founded
2009
Country
United States
Website
www.everpuredata.com
Vendor Details
Company Name
Ant Group
Founded
2014
Country
China
Website
developer.ant-ling.com/en/docs/models/ling/
