Rent NVIDIA H200 (141GB)

Deploy dedicated NVIDIA H200 (141GB) infrastructure with fully managed drivers, storage, and runtime environment optimized for AI workloads.

Ready in 2-5 minutes Pre-configured with AI stack
Free $1 credit Discounted first hour prices
Full Root Access SSH, Docker, custom installs
Launch On Demand
Rent Long term
Regular Price Per hour, billed per second
$4.55

Why Qubrid VM

SSH Access

ACCESS VIA SSH - FULL ACCESS TO GPU FOR RAW PERFORMANCE

Auto Stop

AUTOSTOP YOUR INSTANCE AFTER A SELECTED TIME TO SAVE COST

On-Demand Availability

USE YOUR GPU WHEN YOU WANT, DONT PAY FOR EXTRA COMPUTE

Configurable Storage

CHOOSE FROM A WIDE VARIETY OF HIGH SPEED STORAGE

AI/ML Templates

CHOOSE FROM POPULAR FRAMEWORKS: PYTORCH, TENSORFLOW, ETC.

Quick Launch

INSTANCE READY IN 2-5 MINUTES WITH PRE-CONFIGURED TEMPLATES

Technical Specifications

Enterprise-grade hardware configuration

COMPUTE

GPU Type NVIDIA H200 (141GB)
GPU Count 8 Max
GPU Memory High-Bandwidth
vCPU Cores 128 Max

MEMORY & STORAGE

System RAM 1600 GB Max
Storage Upto 2 TB
Storage Type NVME SSD

NETWORK & SOFTWARE

Network Speed 25 GBPS
CUDA Driver 12.4
OS Version Ubuntu 24.04

Why Choose NVIDIA H200 Scribble

AI + HPC

Good fit for mixed AI and high-performance compute workloads in research and enterprise settings

Glowing brain on a computer chip and gear, symbolizing AI and high-performance compute workloads

Production Scale

Reliable for long-running, enterprise GenAI platforms requiring stability and consistent performance

Isometric cluster of nine server racks representing production-scale GenAI infrastructure

LLM Serving

Well suited for large language model inference with high request volumes and steady throughput needs

Monitor, server, and AI chip with data flow, representing LLM inference and high request volumes

High Memory

Expanded memory footprint supports larger models, longer prompts, and richer retrieval context windows

Laptop and server with data patterns, representing expanded memory for larger models and context

RAG Optimized

Performs especially well in retrieval-augmented generation and vector database backed AI systems

Monitors with landscape and document, AI chip and data flow for RAG and vector database AI

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