High-Performance GPU Hosting for AI/ML Workloads
Train models, run inference, and scale your AI workloads with NVIDIA GPUs. Pre-configured environments, expert support, and flexible pricing.
Choose Your GPU
From development to production, we have the right GPU for your workload.
NVIDIA H200
141GB HBM3eMaximum memory GPU with 141GB HBM3e. Run 70B+ models without quantization. India's largest H200 deployment.
NVIDIA H100
80GB HBM3Flagship AI training GPU with 80GB HBM3. 3x faster than A100 for LLM training with FP8 support.
NVIDIA A100
40GB / 80GB HBM2eIndustry-standard workhorse with excellent price-performance. MIG support for flexible deployment.
NVIDIA L40S
48GB GDDR6Ada Lovelace architecture optimized for inference. 733 TFLOPS FP8 at 40% lower cost than H100.
NVIDIA L4
24GB GDDR6India's most affordable GPU cloud. 24GB memory at just ₹49/hr for budget-friendly inference.
Prices in INR per hour. Reserved instances and volume discounts available — talk to us for custom pricing.
GPU Quick Comparison
Find the right GPU for your workload at a glance.
| GPU | Memory | Bandwidth | Best For | Starting At |
|---|---|---|---|---|
| H200 | 141GB HBM3e | 4.8 TB/s | Large LLM serving, 70B+ models | ₹300/hr |
| H100 SXM | 80GB HBM3 | 3.35 TB/s | LLM training, fine-tuning | ₹249/hr |
| A100 | 40GB / 80GB HBM2e | 2 TB/s | General ML, inference APIs | ₹170/hr (80GB: ₹220) |
| L40S | 48GB GDDR6 | 864 GB/s | Production inference, video AI | ₹150/hr |
| L4 | 24GB GDDR6 | 300 GB/s | 7B models, prototyping, edge AI | ₹49/hr |
Everything You Need to Train and Deploy
Infrastructure built for machine learning workloads.
Pre-installed Frameworks
PyTorch, TensorFlow, CUDA, and popular ML frameworks ready out of the box.
Next Gen NVLink
Ultra-high-speed GPU interconnects for distributed training across multiple GPUs.
Multi-GPU Clusters
Scale from single GPU to 8x GPU clusters with high-speed interconnects.
Persistent Storage
Fast NVMe storage for large datasets with quick access and optimized I/O.
Jupyter Notebooks
Built-in notebook environments for data science and collaborative ML development.
Expert Support
Our team understands ML workloads. Get help with setup, optimization, and debugging.
Power Your AI Workloads
From training to production, GPU cloud accelerates every stage.
Training LLM Models
Train and fine-tune large language models like LLaMA, Mistral, and custom foundation models on multi-GPU clusters.
Running AI APIs
Deploy AI models as production APIs — chatbots, embeddings, image generation — with consistent low latency and high throughput.
Computer Vision Workloads
Real-time object detection, image classification, medical imaging, and autonomous vehicle perception at scale.
Video AI Processing
Video analytics, content moderation, deepfake detection, and real-time video transcription and translation.
Common Questions
How do I choose the right GPU for my workload?
For 70B+ models: H200 (141GB). For training: H100 (₹249/hr) or A100 (₹170/hr). For inference: L40S (₹150/hr) or L4 (₹49/hr). L4 is great for 7B models and development. See individual GPU pages for detailed comparisons.
How quickly can I get started?
Talk to our team on WhatsApp and get your GPU environment provisioned within hours. Pre-configured ML environments with PyTorch, TensorFlow, and CUDA ready to go.
Do you offer multi-GPU instances?
Yes. We offer configurations from single GPU to 8x GPU clusters with high-speed NVLink interconnects. India's largest H200 and H100 deployments available.
What ML frameworks are supported?
All major frameworks: PyTorch, TensorFlow, JAX, Hugging Face Transformers, and more. Pre-configured environments or install your own with full root access.
Is reserved pricing available?
Yes. Monthly reserved instances save ~15-17% vs on-demand. 3-month commits save ~25-30%. Contact us for custom quotes on larger deployments.
Why INR pricing?
INR-denominated billing eliminates forex risk and makes budgeting predictable. No surprise costs from currency fluctuations.
Start Your GPU Project Today
Talk to our GPU team directly. We'll help you choose the right GPU and get your environment running — usually within hours.