NVIDIA RTX A4000 Cloud GPU

NVIDIA RTX A4000 – Cloud GPU for AI & graphics

NVIDIA RTX A4000 at HiTechCloud – a high-performance Cloud GPU for AI, machine learning, rendering and graphics processing. A flexible, capable and cost-efficient cloud solution for enterprises.

1–8 GPU 16 GB VRAM/GPU Up to 32 Core AI & Graphics
RTX Ampere GPU

A4000 Cloud GPU for AI, machine learning and professional graphics

NVIDIA RTX A4000 on HiTechCloud provides flexible dedicated GPUs for businesses that need rendering, AI inference, media processing and graphics workflow infrastructure without investing in physical hardware.

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NVIDIA RTX A4000 Cloud GPU

A high-performance cloud GPU for AI, machine learning, rendering, and professional graphics.

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16 GB VRAM/GPU

Each GPU has 16 GB of VRAM, suited to modeling, media processing, inference and enterprise graphics workloads.

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Scale to 8 GPUs

Configurations from 1 to 8 GPUs let you scale resources with project needs and budget.

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Cost optimization

A flexible, powerful and cost-effective cloud solution for businesses that need a dedicated GPU.

NVIDIA RTX A4000 pricing

Choose an A4000 configuration for AI, rendering and graphics workloads

3223

A4000 x2

NVIDIA A4000

31,104,000 VND / 1 month
  • GPU2 GPU
  • Core16 Core
  • RAM90 GB RAM
  • VRAM32 GB VRAM
  • P2PP2P: No
  • Disk500 GB Up to
  • TypeDedicated
  • NotesNVIDIA RTX A4000 Cloud GPU
Sign up now
3224

A4000 x4

NVIDIA A4000

62,208,000 VND / 1 month
  • GPU4 GPU
  • Core32 Core
  • RAM180 GB RAM
  • VRAM64 GB VRAM
  • P2PP2P: No
  • Disk500 GB Up to
  • TypeDedicated
  • NotesNVIDIA RTX A4000 Cloud GPU
Sign up now
3225

A4000 x8

NVIDIA A4000

72,511,200 VND / 1 month
  • GPU8 GPU
  • Core32 Core
  • RAM192 GB RAM
  • VRAM128 GB VRAM
  • P2PP2P: No
  • Disk2000 GB Up to
  • TypeDedicated
  • NotesNVIDIA RTX A4000 Cloud GPU
Sign up now
GPU Infrastructure

Flexible enough for AI, machine learning, rendering, and graphics processing

NVIDIA RTX A4000 lets businesses deploy cloud GPU quickly, control cost and scale with actual demand.

Suited to AI and ML

Supports small-scale training, inference, computer vision, data processing and AI model experiments.

Strong for graphics

Optimized for rendering, CAD, 3D visualization, media workflows and remote graphics applications.

Flexible deployment

Choose 1, 2, 4 or 8 GPUs based on workload size, session count and budget.

Dedicated GPU

Dedicated resources keep workloads consistent, make performance easier to control, and suit production environments.

A range of billing cycles

Terms from 1 to 60 months, making cost planning straightforward.

Technical support

HiTechCloud advises on drivers, CUDA, render environments, AI frameworks, and the right configuration.

Use cases

Scenarios suited to the NVIDIA RTX A4000

AI/ML AI and machine learning

Run inference, small-scale training, computer vision, OCR and model experimentation workflows.

Render Rendering and graphics processing

Suited to modeling, 3D rendering, media processing, design and visualization.

Cloud GPU GPU cloud for business

Deploy a dedicated GPU flexibly, with no upfront hardware investment.

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High-performance GPU instances for a wide range of workloads

Powerful capacity, optimized to accelerate AI/ML and high-performance workloads at any scale.

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A broad range of infrastructure options

Training, inference, or fine-tuning - HiTechCloud offers a range of GPUs to match your needs, with transparent pricing and on-demand deployment environments.

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Built to NVIDIA reference architecture

HiTechCloud GPU instances combine NVLink/PCIe, InfiniBand (RDMA), and RAIL topology to optimize AI/HPC performance.

GPU cloud architecture

Three core connectivity layers for high-performance GPU clusters

A network and GPU fabric designed so AI workloads scale reliably, easing bandwidth bottlenecks and holding performance at scale.

NVLink and PCIe Switch for HiTechCloud GPU instances

NVLink / PCIe Switch

High-speed GPU-to-GPU connectivity within and between nodes, reducing bottlenecks during model training.

InfiniBand RDMA for distributed training

InfiniBand (RDMA)

Low-latency connectivity optimized for distributed training and reduced processing load on the host.

RAIL topology for high-performance GPU clusters

RAIL Topology

A parallel network architecture delivering higher bandwidth, redundancy, and consistent performance at any scale.

Auto scaling

GPU auto scaling and utilization optimization

From a single GPU to large clusters, HiTechCloud provisions resources ahead of demand and gets the most out of every instance.

GPU auto scaling with Kubernetes

Auto scaling with Kubernetes

Scale GPU resources automatically from a handful to thousands of GPUs, with forecasting and provisioning ahead of demand.

SSH, TCP, and HTTP connection security for GPU instances

Security for every access connection

Access over SSH, TCP, and HTTP with built-in protection layers that keep enterprise data safe and access fully controlled.

Optimize GPU utilization with MIG

Optimize GPU utilization with MIG

Partition a single GPU into multiple independent instances to run AI workloads in parallel, improving utilization and lowering infrastructure cost.

GPU instance

Transparent pricing, no hidden fees

From large-scale training to real-time inference - pay only when you actually run, on a GPU cloud platform built for every AI and high-performance workload.

Ready to launch your first GPU instance?

From sign-up to a running GPU instance in under 5 minutes - no complex setup, no resource reservations, no charges while idle. Just deploy, run, and pay only for what you use.

The HiTechCloud ecosystem

More than compute - manage, scale, and build everything on one straightforward cloud ecosystem.

AgentBase

A complete management platform for deploying and operating AI agents securely at scale on enterprise-grade infrastructure.

Explore AgentBase

AI Platform

A unified platform for training, fine-tuning, and deploying AI models at any scale.

Explore AI Platform

Vector Database

Supports fast search, real-time analytics, log and large-scale event data, and vector databases for RAG.

Explore Vector Database

Kubernetes

Managed Kubernetes for container orchestration, AI services, and GPU cloud workloads.

Explore Kubernetes
Southeast Asia

Scale with confidence across Southeast Asia

Deploy systems and applications closer to your users, reducing latency and meeting local regulatory requirements.

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Bangkok

BKK-01

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Ho Chi Minh

HCM-01 · HCM-02 · HCM-03

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Ha Noi

HAN-01 · HAN-02

Southeast Asia regional map for HiTechCloud infrastructure
1,000+ businesses

A partner for your digital transformation journey

Large enterprises and fast-growing startups choose HiTechCloud for secure, high-performance AI cloud solutions that let them innovate and scale.

Have a specific requirement? HiTechCloud is ready to help.

The HiTechCloud team advises on GPU architecture, networking, security, and an operating model matched to your real workloads.

FAQ

FAQ

Quick facts before choosing an NVIDIA RTX A4000 configuration at HiTechCloud.

Which workloads is the NVIDIA RTX A4000 suited to?

Suited to AI, machine learning, inference, rendering, CAD, media processing, and professional graphics on a cloud GPU platform.

When should you choose A4000 x4 or x8?

Choose a multi-GPU configuration when you need to run several workloads in parallel, handle large rendering jobs, or need more total VRAM.

Which environments is the RTX A4000 suited for?

Suits cloud workstations, 3D design, CAD, modeling, AI demos, and small-scale inference at a reasonable cost.

Does RTX A4000 support CUDA?

Yes. HiTechCloud provides a suitable CUDA environment, drivers and frameworks for AI, rendering or light compute workloads.

When should you upgrade to the A5000?

When you need more VRAM, heavier rendering, more parallel tasks or AI workloads beyond what the RTX A4000 can handle.

What billing cycles are available?

Plans are available on 1-month, 3-month, 6-month, 12-month, and long-term cycles.

Cloud GPU Ready

Need advice on an NVIDIA RTX A4000 configuration for your workload?

HiTechCloud helps you select the GPU count, cores, RAM, storage, drivers, CUDA version, render environment, and AI framework to match your deployment.