Hourly
H200 NVL
141 GB HBM3e · 4.8 TB/s · 1–4x GPUs
Largest models on the fewest GPUs
Training and large models
HBM memory and multi-GPU scale for LLMs and HPC
H100 HGX
80 GB HBM3 · 3.35 TB/s · 1–8x GPUs
Distributed training, LLMs and HPC
A100
80 GB HBM2e · 2.0 TB/s · 1–4x GPUs
Training, analytics and HPC
Blackwell
NVIDIA’s newest architecture with GDDR7 memory
Hourly
RTX PRO 6000
96 GB GDDR7 · 1.79 TB/s · 1–4x GPUs
Generative AI, inference and heavy rendering
New
RTX PRO 4500
32 GB GDDR7 · 800 GB/s · 1–8x GPUs
Lowest-cost Blackwell for inference, graphics and VDI
Inference, rendering and VDI
Cost-efficient GPUs for serving models and graphics
Hourly
L40S
48 GB GDDR6 · 864 GB/s · 1–4x GPUs
Fine-tuning, LLM inference and rendering
L4
24 GB GDDR6 · 300 GB/s · 1–4x GPUs
Low-power inference and video
RTX 6000 Ada
48 GB GDDR6 · 960 GB/s · 1–2x GPUs
Rendering and visualisation, with room for AI
RTX A6000
48 GB GDDR6 · 768 GB/s · 1–2x GPUs
Simulation, CAD and 3D rendering
A30
24 GB HBM2 · 933 GB/s · 1–2x GPUs
Mainstream inference and virtualisation
A2
16 GB GDDR6 · 200 GB/s · 1–2x GPUs
Entry-level inference and edge AI
GPU Pricing Calculator
Compare monthly GPU costs across AceCloud, AWS, Azure and Google Cloud using live pricing data.
GPU count
| Provider | Nearest equivalent | Monthly cost | vs. AceCloud |
|---|---|---|---|
- GPU pricing We calculate the price per GPU, then multiply it by the number you select. Hyperscalers may require you to rent a full server, often with eight GPUs, even if you need fewer. AceCloud offers single-GPU options.
- Final pricing. Prices and discounts vary by region, usage volume, and commitment period for all providers, including AceCloud.
- Additional costs Storage, outgoing data transfer (egress), and support charges are not included in these estimates.
- A month is 730 hours.
Frequently Asked Questions
The cost of renting a cloud GPU in India depends on the NVIDIA GPU model, GPU memory (VRAM), vCPU, RAM, and rental duration. AceCloud offers GPU cloud instances with flexible hourly and monthly pricing. You can choose from NVIDIA A2, A30, L4, L40S, A100, H100, and H200 GPUs based on your performance requirements and budget.
AceCloud offers a range of NVIDIA GPUs, including H200, H100, A100, L40S, L4, A30, A2, RTX PRO 4500 Blackwell, RTX PRO 6000, RTX 6000 Ada, RTX A6000, and RTX 8000. These GPUs support different workloads, including AI model training, generative AI, LLM inference, deep learning, HPC, and 3D rendering.
NVIDIA H100 and H200 GPUs are designed for demanding AI training, large language models (LLMs), and high-performance inference workloads. The A100 is suitable for AI/ML training and HPC applications, while the L40S is optimized for generative AI, graphics, rendering, and inference workloads. The right GPU depends on your model size, VRAM requirements, performance targets, and budget.
Yes. AceCloud provides cloud GPU infrastructure powered by NVIDIA GPUs for AI model training, fine-tuning, deep learning, and inference. You can choose GPUs such as NVIDIA H100, H200, A100, and L40S to run workloads involving large language models, generative AI, computer vision, and machine learning.
Yes. AceCloud offers flexible GPU rental options, including hourly and monthly billing, allowing businesses to select a plan based on workload requirements. Longer-term plans are also available, with approximately 5% savings on six-month plans and 10% savings on twelve-month plans. This helps businesses manage GPU infrastructure costs without investing in physical GPU hardware upfront.
AceCloud provides dedicated GPU instances with direct GPU passthrough to virtual machines. This allows users to access dedicated GPU resources rather than sharing GPU compute capacity with other customers. Supported NVIDIA GPUs can also offer Multi-Instance GPU (MIG) capabilities for workloads that require GPU partitioning.
Yes. AceCloud supports multi-GPU configurations and GPU clusters to accommodate growing computational requirements. You can scale vertically by choosing instances with multiple NVIDIA GPUs or horizontally by connecting multiple GPU nodes through Kubernetes. This makes it easier to support distributed AI model training, large-scale inference, deep learning, and other HPC workloads.