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What is VLLM?

VLLM is an innovative library specifically designed for the efficient inference and deployment of Large Language Models (LLMs). Originally developed at UC Berkeley's Sky Computing Lab, it has evolved into a collaborative project that benefits from input by both academia and industry. The library stands out for its remarkable serving throughput, achieved through its unique PagedAttention mechanism, which adeptly manages attention key and value memory. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, leveraging technologies such as FlashAttention and FlashInfer to enhance model execution speed significantly. In addition, VLLM accommodates several quantization techniques, including GPTQ, AWQ, INT4, INT8, and FP8, while also featuring speculative decoding capabilities. Users can effortlessly integrate VLLM with popular models from Hugging Face and take advantage of a diverse array of decoding algorithms, including parallel sampling and beam search. It is also engineered to work seamlessly across various hardware platforms, including NVIDIA GPUs, AMD CPUs and GPUs, and Intel CPUs, which assures developers of its flexibility and accessibility. This extensive hardware compatibility solidifies VLLM as a robust option for anyone aiming to implement LLMs efficiently in a variety of settings, further enhancing its appeal and usability in the field of machine learning.

What is Amazon SageMaker Feature Store?

Amazon SageMaker Feature Store is a specialized, fully managed storage solution created to store, share, and manage essential features necessary for machine learning (ML) models. These features act as inputs for ML models during both the training and inference stages. For example, in a music recommendation system, pertinent features could include song ratings, listening duration, and listener demographic data. The capacity to reuse features across multiple teams is crucial, as the quality of these features plays a significant role in determining the precision of ML models. Additionally, aligning features used in offline batch training with those needed for real-time inference can present substantial difficulties. SageMaker Feature Store addresses this issue by providing a secure and integrated platform that supports feature use throughout the entire ML lifecycle. This functionality enables users to efficiently store, share, and manage features for both training and inference purposes, promoting the reuse of features across various ML projects. Moreover, it allows for the seamless integration of features from diverse data sources, including both streaming and batch inputs, such as application logs, service logs, clickstreams, and sensor data, thereby ensuring a thorough approach to feature collection. By streamlining these processes, the Feature Store enhances collaboration among data scientists and engineers, ultimately leading to more accurate and effective ML solutions.

Media

Media

Integrations Supported

AWS Glue
AWS Lake Formation
Amazon Athena
Amazon Kinesis
Amazon Redshift
Amazon S3
Amazon SageMaker
Amazon SageMaker Data Wrangler
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Apache Spark
Databricks Data Intelligence Platform
Docker
Hugging Face
Kubernetes
NGINX
NVIDIA DRIVE
OpenAI
PyTorch
Snowflake

Integrations Supported

AWS Glue
AWS Lake Formation
Amazon Athena
Amazon Kinesis
Amazon Redshift
Amazon S3
Amazon SageMaker
Amazon SageMaker Data Wrangler
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Apache Spark
Databricks Data Intelligence Platform
Docker
Hugging Face
Kubernetes
NGINX
NVIDIA DRIVE
OpenAI
PyTorch
Snowflake

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

VLLM

Company Location

United States

Company Website

docs.vllm.ai/en/latest/

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/sagemaker/feature-store/

Categories and Features

Categories and Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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