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Average Ratings 0 Ratings

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features
design
support

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Write a Review

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

Build, test and optimize Generative AI apps that unlock the value in your data. Our industry-leading ML expertise, our state-of-the art test and evaluation platform and advanced retrieval augmented-generation (RAG) pipelines will help you optimize LLM performance to meet your domain-specific needs. We provide an end-toend solution that manages the entire ML Lifecycle. We combine cutting-edge technology with operational excellence to help teams develop high-quality datasets, because better data leads better AI.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon S3 No 
Azure Blob Storage No 
Azure Marketplace No 
Claude No 
Coral No 
Diffgram Data Labeling No 
Google Cloud Storage No 
Google Docs No 
OpenAI No 
Pilot No 
Unremot No 

Integrations

Amazon S3 Yes 
Azure Blob Storage Yes 
Azure Marketplace Yes 
Claude Yes 
Coral Yes 
Diffgram Data Labeling Yes 
Google Cloud Storage Yes 
Google Docs Yes 
OpenAI Yes 
Pilot Yes 
Unremot Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial Yes 
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) Yes 
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

Scale AI

Founded

2016

Country

United States

Website

scale.com

Product Features

Artificial Intelligence

Chatbot No 
For Healthcare Yes 
For Sales Yes 
For eCommerce Yes 
Image Recognition Yes 
Machine Learning Yes 
Multi-Language Yes 
Natural Language Processing Yes 
Predictive Analytics Yes 
Process/Workflow Automation Yes 
Rules-Based Automation Yes 
Virtual Personal Assistant (VPA) No 

Machine Learning

Deep Learning Yes 
ML Algorithm Library Yes 
Model Training Yes 
Natural Language Processing (NLP) Yes 
Predictive Modeling Yes 
Statistical / Mathematical Tools No 
Templates Yes 
Visualization Yes 

Natural Language Processing

Co-Reference Resolution Yes 
In-Database Text Analytics Yes 
Named Entity Recognition Yes 
Natural Language Generation (NLG) Yes 
Open Source Integrations No 
Parsing Yes 
Part-of-Speech Tagging Yes 
Sentence Segmentation Yes 
Stemming/Lemmatization No 
Tokenization No 

Alternatives

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