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What is kluster.ai?

Kluster.ai serves as an AI cloud platform specifically designed for developers, facilitating the rapid deployment, scalability, and fine-tuning of large language models (LLMs) with exceptional effectiveness. Developed by a team of developers who understand the intricacies of their needs, it incorporates Adaptive Inference, a flexible service that adjusts in real-time to fluctuating workload demands, ensuring optimal performance and dependable response times. This Adaptive Inference feature offers three distinct processing modes: real-time inference for scenarios that demand minimal latency, asynchronous inference for economical task management with flexible timing, and batch inference for efficiently handling extensive data sets. The platform supports a diverse range of innovative multimodal models suitable for various applications, including chat, vision, and coding, highlighting models such as Meta's Llama 4 Maverick and Scout, Qwen3-235B-A22B, DeepSeek-R1, and Gemma 3. Furthermore, Kluster.ai includes an OpenAI-compatible API, which streamlines the integration of these sophisticated models into developers' applications, thereby augmenting their overall functionality. By doing so, Kluster.ai ultimately equips developers to fully leverage the capabilities of AI technologies in their projects, fostering innovation and efficiency in a rapidly evolving tech landscape.

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.

Media

Media

Integrations Supported

OpenAI
Database Mart
DeepSeek R1
DeepSeek-V3
Docker
Gemma 3
Hugging Face
KServe
Kubernetes
LLM Gateway
Llama
Llama 4 Maverick
Llama 4 Scout
Mistral NeMo
NGINX
NVIDIA DRIVE
PyTorch
Qwen
Qwen2.5-VL
Qwen3

Integrations Supported

OpenAI
Database Mart
DeepSeek R1
DeepSeek-V3
Docker
Gemma 3
Hugging Face
KServe
Kubernetes
LLM Gateway
Llama
Llama 4 Maverick
Llama 4 Scout
Mistral NeMo
NGINX
NVIDIA DRIVE
PyTorch
Qwen
Qwen2.5-VL
Qwen3

API Availability

Has API

API Availability

Has API

Pricing Information

$0.15per input
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

kluster.ai

Date Founded

2024

Company Location

United States

Company Website

www.kluster.ai/

Company Facts

Organization Name

VLLM

Company Location

United States

Company Website

docs.vllm.ai/en/latest/

Categories and Features

Categories and Features

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