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Average Ratings 0 Ratings

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ease
features
design
support

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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 No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon S3 No 
Microsoft Hyper-V No 
OpenStack No 
Swift No 
VMware Cloud No 

Integrations

Amazon S3 Yes 
Microsoft Hyper-V Yes 
OpenStack Yes 
Swift Yes 
VMware Cloud Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
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 Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

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

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

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

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