Storage
NVMe local scratch, RAID0/10 options, object storage integration for datasets and checkpoints.
Networking
Private links, dedicated bandwidth tiers, Anycast IPs, and edge acceleration for inference endpoints.
Observability
GPU/CPU/memory metrics, per‑job logs, tracing for inference paths, and budget alerts.
Security
IAM/RBAC, MFA/SSO, network policies, encrypted volumes, secrets management, and audit logging.
Why HostGenX for GPUs
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Built for AI speed: High‑throughput PCIe/ NVLink, fast NVMe scratch, and tuned drivers for peak training and inference
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Freedom to scale: Spin up cloud GPUs in minutes, or lock in steady performance with dedicated or bare metal.
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Cost clarity: Pay per hour or reserve monthly; use budgets, alerts, and recommendations to stay on target.
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Secured by design: Private networking, isolation options, disk encryption, and role‑based access.
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Developer-friendly: One-click templates, Terraform modules, and CI/CD integrations to speed setup and streamline MLOps.
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Operate with confidence: 24/7 monitoring, proactive incident response, and clear SLAs to keep workloads reliable.
Dedicated GPU
Single‑tenant machines for consistent throughput, full control, and predictable cost—ideal for long‑running jobs and production stacks.
Bare Metal GPU
Direct‑to‑hardware performance, no hypervisor overhead, and maximum customization for kernels, drivers, and NIC offloads.
Virtual/Cloud GPU
Elastic capacity for experiments, bursty training, and scale‑out inference; perfect for CI/CD pipelines and dev/testing.
Managed GPU Clusters
Kubernetes‑ready deployments with autoscaling, node pools, and GPU scheduling for multi‑team environments.
Purpose‑Built GPU Infrastructure
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Direct‑to‑silicon performance: Bare‑metal GPUs remove hypervisor overhead for deterministic latency, full PCIe bandwidth, and no noisy‑neighbor interference.
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Elastic scale on demand: Virtual/cloud GPU adds burst capacity for experiments and spikes without long provisioning or upfront hardware costs.
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Built for AI workloads: CUDA/Tensor cores with high‑bandwidth VRAM and fast interconnects accelerate matrix math, rendering, and simulation end‑to‑end.
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Operationally simple and controlled: Choose single‑tenant for compliance and predictability or managed clusters for autoscaling, quotas, and team scheduling.
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.
Your stack, production‑ready
From model development to serving and automation, the platform supports mainstream AI frameworks, GPU‑optimized runtimes, and containerized delivery with repeatable deployments across environments.
Frameworks
PyTorch, TensorFlow, JAX, RAPIDS, Triton Inference, vLLM, TensorRT.
Tooling
CUDA/cuDNN, ROCm (where applicable), Docker + NVIDIA Container Toolkit, Helm charts, Terraform modules.
Orchestration
Managed Kubernetes, autoscaling node pools, spot/on‑demand mixes, and GitOps workflows.
Scale‑up training blueprints
Harden GPU workloads with tenant isolation, end‑to‑end encryption, managed keys, access audits, and Git‑driven change controls—keeping environments compliant and traceable.
Isolation options
Enforce tenant boundaries with single‑tenant GPU nodes, dedicated NICs, and private routing/VLANs to prevent cross‑tenant traffic and noisy‑neighbor effects.
Data protection
Apply end‑to‑end encryption in transit and at rest with centralized key management and auditable access logs to safeguard sensitive data.
Governance
Use policy‑as‑code and Git‑driven change control to enforce guardrails, approvals, and environment separation across dev, staging, and prod.
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.
