GPU Performance
Harness cutting-edge GPU power to accelerate AI and ML workloads. Faster GPUs mean quicker model training, optimized performance, and reduced operational costs.
Scalability & Flexibility
Effortlessly scale compute resources as your AI projects grow. Our infrastructure adapts to your changing workloads, ensuring consistent performance and cost efficiency.
High-Speed Data Storage
Experience lightning-fast data access with NVMe storage engineered for AI workloads. Eliminate storage bottlenecks and accelerate model training and inference cycles.
Ultra-Low Latency Network
Achieve real-time data processing with our high-speed, carrier-neutral network. Designed for edge AI and analytics that demand near-zero latency.
High-performance AI Solutions
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Latest Generation CPUs: Intel Xeon/AMD EPYC with high core counts
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Advanced GPU Options: NVIDIA H200, A100, H100, RTX 4090, or equivalent
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Memory: 128GB–6TB DDR5 ECC RAM options
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Storage: NVMe SSD (up to 12 drives), SATA, hybrid storage
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Networking: Dual 10GbE or 100GbE LAN, IPMI for management
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Power: Redundant, high-efficiency (80 PLUS Platinum) PSUs
Production‑Ready AI Infrastructure
Ship reliably with opinionated stacks for distributed training, memory‑efficient fine‑tuning, autoscaled low‑latency endpoints, and streaming data paths that minimize stalls and maximize throughput.
Training
Multi‑GPU, mixed precision, and distributed training patterns out of the box; fast checkpointing on NVMe.
Fine‑tuning
Efficient LoRA/QLoRA pipelines and curated environments for popular frameworks.
Inference
Low‑latency endpoints with tensor parallelism and model caching to cut serving costs.
Data pipelines
High‑IO ingest and feature stores with local caching to keep GPUs fed.
Built for the Future. Ready to Scale.
Empowering businesses with future-ready infrastructure that scales effortlessly — enabling startups and enterprises to grow fearlessly without limits.
Train Efficiently
Kickstart model development with multi-GPU clusters optimized for parallel AI & ML training. Reduce iteration time and accelerate experimentation — whether you’re fine-tuning AI models or training from scratch.
Scale Intelligently
As your models grow, HostGenX scales with you. Leverage NVMe storage for lightning-fast data access and high-bandwidth networking to keep massive datasets flowing smoothly across nodes.
Deploy Confidently
Move from the lab to live environments effortlessly. Our unified AI hosting infrastructure ensures consistent performance, reliability, and speed — so you can deploy production-ready AI & ML systems with confidence.
Scale‑up training blueprints
Train large models
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Hardware: Multi‑GPU (e.g., H100/L40S/A100 class), high‑core CPU, 256–1024 GB RAM, NVMe RAID.
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Network: 25–100 Gbps options, private VLAN/VPC, reserved egress lanes.
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Notes: Pre‑baked CUDA images, NCCL tuning, distributed training templates.
41%
Lower tail latency on inference APIs with tensor parallelism and on‑node model caching.
63%
Faster model rollout cycles using containerized builds and GitOps‑driven deploys across GPU clusters.
70%
Shorter time‑to‑first‑token via warmed weights, KV‑cache reuse, and autoscaled GPU serving layers.
Built in India, Built for Global Growth
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Strategic Location: Low-latency connectivity across Asia-Pacific.
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Regulatory Compliance: Meets Indian IT & Data Protection standards.
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Enterprise-Grade Security: 24/7 monitoring, biometric access, and advanced firewalls.
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Green Infrastructure: Energy-efficient cooling and renewable energy adoption.
Real Experiences. Real Results.
Trusted by startups and enterprises alike for secure, scalable infrastructure.
Migrating our business website to HostGenX was the best decision we made. The uptime and speed are phenomenal, and their support team always goes the extra mile.
We needed a reliable hosting partner who could handle high traffic campaigns, and HostGenX delivered flawlessly. Our digital campaigns now run without a single glitch.
With HostGenX colocation, our data is housed in a secure, compliant environment. The redundant power and cooling systems give us complete peace of mind.
HostGenX GPU servers have drastically cut down our AI model training time. Performance, scalability, and affordability — all in one package.
HostGenX cloud hosting has been a game-changer for our SaaS platform. Scalability is smooth, and we can deploy new environments within minutes.
Running SAP on HostGenX infrastructure gave us enterprise-level performance at a fraction of the cost. Their SAP expertise is truly impressive.
For ML workloads, their GPU hosting is unmatched. We get enterprise-grade GPUs with seamless scaling, helping us deliver projects faster.
Their colocation service is simply world-class. The 24/7 monitoring and security standards are exactly what our healthcare business required.
Quick Answers, Clear Solutions
Explore our FAQs to better understand how HostGenX helps you scale with confidence.
1.What is GPU hosting?
GPU hosting provides servers equipped with graphics processors for massively parallel workloads like AI/ML, deep learning, LLM inference, rendering, and data analytics. It accelerates compute-heavy tasks compared to CPU-only servers.
2.When should GPU hosting be chosen over CPUs?
Pick GPUs when training or serving neural networks, running computer vision, accelerating data science pipelines, or rendering—any workload that benefits from parallel execution. CPUs still suit control logic, databases, and general web workloads.
3.Can existing workflows run as‑is?
Yes. Containerized environments with CUDA/ROCm images and framework presets; bring custom containers or start from curated images.
4.What frameworks and tools are supported?
Popular stacks like PyTorch, TensorFlow, JAX, RAPIDS, CUDA/cuDNN, ROCm (where applicable), Docker with NVIDIA Container Toolkit, Triton Inference, and vLLM are typically supported. Prebuilt images can speed up setup.
5.How are costs controlled?
Budgets and alerts, right-sizing recommendations, mixed precision and batch tuning, autoscaling for inference, and commitments for steady workloads. Pick on-demand for experiments and reserved capacity for production.
6.How is data handled for large datasets and checkpoints?
Use a mix of fast local NVMe for active training data and checkpoints, plus object storage for datasets and archives. For distributed training, ensure high-throughput networking and tuned I/O pipelines.
