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Average Ratings 0 Ratings
Description
The Granica AI efficiency platform significantly lowers the expenses associated with storing and accessing data while ensuring its privacy, thus facilitating its use for training purposes. Designed with developers in mind, Granica operates on a petabyte scale and is natively compatible with AWS and GCP. It enhances the effectiveness of AI pipelines while maintaining privacy and boosting performance. Efficiency has become an essential layer within the AI infrastructure. Using innovative compression algorithms for byte-granular data reduction, it can minimize storage and transfer costs in Amazon S3 and Google Cloud Storage by as much as 80%, alongside reducing API expenses by up to 90%. Users can conduct an estimation in just 30 minutes within their cloud environment, utilizing a read-only sample of their S3 or GCS data, without the need for budget allocation or total cost of ownership assessments. Granica seamlessly integrates into your existing environment and VPC, adhering to all established security protocols. It accommodates a diverse array of data types suitable for AI, machine learning, and analytics, offering both lossy and fully lossless compression options. Furthermore, it has the capability to identify and safeguard sensitive data even before it is stored in your cloud object repository, ensuring compliance and security from the outset. This comprehensive approach not only streamlines operations but also fortifies data protection throughout the entire process.
Description
LMCache is an innovative open-source Knowledge Delivery Network (KDN) that functions as a caching layer for serving large language models, enhancing inference speeds by allowing the reuse of key-value (KV) caches during repeated or overlapping calculations. This system facilitates rapid prompt caching, enabling LLMs to "prefill" recurring text just once, subsequently reusing those saved KV caches in various positions across different serving instances. By implementing this method, the time required to generate the first token is minimized, GPU cycles are conserved, and throughput is improved, particularly in contexts like multi-round question answering and retrieval-augmented generation. Additionally, LMCache offers features such as KV cache offloading, which allows caches to be moved from GPU to CPU or disk, enables cache sharing among instances, and supports disaggregated prefill to optimize resource efficiency. It works seamlessly with inference engines like vLLM and TGI, and is designed to accommodate compressed storage formats, blending techniques for cache merging, and a variety of backend storage solutions. Overall, the architecture of LMCache is geared toward maximizing performance and efficiency in language model inference applications.
API Access
Has API
API Access
Has API
Integrations
.NET
Amazon S3
Amazon Web Services (AWS)
Go
Google Cloud Platform
Java
JavaScript
PHP
Python
Integrations
.NET
Amazon S3
Amazon Web Services (AWS)
Go
Google Cloud Platform
Java
JavaScript
PHP
Python
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Granica
Website
granica.ai/
Vendor Details
Company Name
LMCache
Country
United States
Website
lmcache.ai/
Product Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)