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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.
Description
Sangfor aStor represents an innovative software-defined storage solution that consolidates block, file, and object storage into a cohesive, elastically scalable resource pool, utilizing a fully symmetrical distributed architecture to facilitate on-demand provisioning of high-performance and cost-effective storage tiers tailored to various service needs. It can be deployed as either an integrated hardware-software system or as standalone software, with the ability to scale from a minimal setup of three commodity x86 nodes to expansive cloud-scale clusters comprising thousands of nodes, allowing for EB-level capacity growth. The system's multi-node parallel processing and intelligent caching mechanisms—including RDMA, SSD hot-data caching, and layering—achieve exceptional throughput, IOPS, and performance with small I/O operations, significantly enhancing cache hit rates to 90% and improving small I/O processing by as much as 65%. Additionally, its distributed metadata management ensures the seamless handling of billions of files without any significant latency, making it a robust solution for modern storage challenges. Overall, Sangfor aStor stands out as a versatile and powerful option for organizations looking to optimize their storage infrastructure.
API Access
Has API
API Access
Has API
Integrations
Amazon S3
Microsoft Hyper-V
OpenStack
Swift
VMware Cloud
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
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
LMCache
Country
United States
Website
lmcache.ai/
Vendor Details
Company Name
Sangfor
Founded
2000
Country
China
Website
www.sangfor.com/cloud-and-infrastructure/products/astor-enterprise-data-storage-solution