NxtGen sovereign GPU instances on demand for Katherine businesses — AI/ML training, LLM fine-tuning, GenAI inference, HPC. Pre-built AI stacks, NVLink multi-GPU, InfiniBand RDMA networking. Sovereign India datacenters.
80 GB HBM · Tensor Cores · NVLink · Bare-metal & vGPU instances
TensorFlow · PyTorch · CUDA · cuDNN · RAPIDS · Jupyter · MLflow
400 Gbps · GPUDirect RDMA · Multi-node distributed training
Data stays in India · No CLOUD Act · DPDPA compliant · INR billing
NxtGen GPU Cloud, branded as SpeedCloud AI, is NxtGen's GPU-as-a-Service platform for AI/ML training, deep learning, LLM fine-tuning, generative AI inference, HPC simulations, and media rendering. It provides on-demand access to NVIDIA A100, H100, L40S, and T4 GPUs — available as bare-metal dedicated servers or virtual GPU instances — from NxtGen's sovereign datacenters in India.
PrecisionTech is an Authorized NxtGen Cloud Partner in Katherine, deploying SpeedCloud AI GPU infrastructure — GPU instance provisioning, NVLink/InfiniBand networking, AI stack configuration, Kubernetes GPU clusters, and 24×7 managed GPU operations. ISO 9001, ISO 27001, CMMI Level 3 certified.
NVIDIA A100, H100, L40S, T4 GPUs · Bare-metal & vGPU ·
| Specification | NVIDIA T4 | NVIDIA L40S | NVIDIA A100 | NVIDIA H100 |
|---|---|---|---|---|
| Architecture | Turing | Ada Lovelace | Ampere | Hopper |
| GPU Memory | 16 GB GDDR6 | 48 GB GDDR6 | 80 GB HBM2e | 80 GB HBM3 |
| Memory Bandwidth | 320 GB/s | 864 GB/s | 2 TB/s | 3.35 TB/s |
| FP16 Tensor TFLOPS | 65 | 366 | 312 | 989 (sparse) |
| Multi-Instance GPU | — | — | ✅ Up to 7 slices | ✅ Up to 7 slices |
| NVLink | — | — | ✅ 600 GB/s | ✅ 900 GB/s |
| Interconnect | PCIe Gen4 | PCIe Gen4 | NVLink + PCIe | NVLink + PCIe Gen5 |
| Bare-Metal Available | ✅ | ✅ | ✅ | ✅ |
| vGPU Available | ✅ | ✅ | ✅ | ✅ |
| Pay-per-Hour | ✅ | ✅ | ✅ | ✅ |
| Reserved Instances | ✅ | ✅ | ✅ | ✅ |
| Ideal For | Inference, Dev/Test | Inference, Rendering | Training, LLM, HPC | LLM Training, GenAI |
| Pricing | Request a Quote — All GPU Tiers | |||
All GPU tiers include persistent NVMe storage, pre-built AI stacks, DDoS protection, and 99.99% SLA. Multi-GPU NVLink configurations available for A100 and H100. Contact PrecisionTech for a detailed GPU cloud cost estimate in INR with GST.
NxtGen GPU Cloud, commercially branded as SpeedCloud AI, is the GPU-as-a-Service offering from NxtGen — India's sovereign cloud platform. SpeedCloud AI provides enterprises, AI startups, research labs, and media studios with on-demand access to NVIDIA data-centre-class GPUs — A100 (Ampere), H100 (Hopper), L40S (Ada Lovelace), and T4 (Turing) — for AI/ML model training, large language model (LLM) fine-tuning, generative AI inference, high-performance computing (HPC), and GPU-accelerated media rendering.
Unlike general-purpose cloud IaaS where GPUs are an add-on, SpeedCloud AI is purpose-built for GPU workloads from the hardware layer up. Every node is engineered with NVLink and NVSwitch GPU-to-GPU interconnects (up to 900 GB/s on H100), InfiniBand or RoCE high-bandwidth networking for multi-node distributed training, persistent NVMe SSD storage for training datasets and model checkpoints, and pre-configured AI software stacks (TensorFlow, PyTorch, CUDA, cuDNN, RAPIDS, Jupyter, MLflow) so teams can start training immediately without spending days on environment setup.
SpeedCloud AI instances are available in two consumption models: bare-metal dedicated GPU servers for maximum performance (direct PCIe access, full NVLink bandwidth, zero hypervisor overhead) and virtual GPU (vGPU) instances using NVIDIA vGPU technology for cost-efficient GPU sharing across inference workloads, development environments, and smaller training runs. Both models support pay-per-hour on-demand pricing and reserved instances for committed usage.
All SpeedCloud AI infrastructure runs in NxtGen's sovereign datacenters in Bengaluru, Mumbai, and Hyderabad — with every byte of training data, model weight, and inference input/output staying under exclusive Indian jurisdiction. As an Authorized NxtGen Cloud Partner in Katherine, PrecisionTech architects, provisions, and manages SpeedCloud AI environments — from GPU selection and multi-GPU networking design through 24×7 managed GPU operations with NVIDIA DCGM monitoring.
80 GB HBM2e GPU memory with 2 TB/s bandwidth and 312 TFLOPS FP16 Tensor Core performance. Ampere architecture with 3rd-gen NVLink (600 GB/s) for multi-GPU scaling. Multi-Instance GPU (MIG) partitions one A100 into up to 7 isolated GPU instances for efficient inference serving. The workhorse for large-scale distributed training, LLM fine-tuning, and production AI workloads.
NVIDIA's flagship Hopper GPU — 80 GB HBM3 memory, 3.35 TB/s bandwidth, 989 TFLOPS FP16 (sparse). 4th-gen NVLink delivers 900 GB/s GPU-to-GPU bandwidth. The Transformer Engine automatically optimises mixed precision for LLM training. PCIe Gen5 support. Up to 3× faster than A100 for transformer-based model training — the definitive GPU for LLM fine-tuning and GenAI workloads.
Ada Lovelace architecture with 48 GB GDDR6 memory and 366 TFLOPS FP16 Tensor Core performance. Optimised for AI inference serving (Triton, TensorRT), real-time rendering (RTX ray tracing), video transcoding (NVENC), and VFX workloads. Cost-effective alternative to A100 for inference-heavy deployments and media production pipelines where memory bandwidth is less critical than compute throughput.
NVLink and NVSwitch interconnects link up to 8 GPUs within a single node into a unified compute domain — 600 GB/s per link on A100, 900 GB/s on H100. NVSwitch provides all-to-all non-blocking fabric enabling every GPU to access every other GPU's memory at full bandwidth. Critical for training models that exceed single-GPU memory (LLMs, vision transformers) using data parallelism, tensor parallelism, and pipeline parallelism.
Dedicated physical GPU servers with direct PCIe access, full NVLink bandwidth, zero hypervisor overhead, and root-level OS control. No noisy neighbours — every TFLOP, every byte of GPU memory, every GB/s of NVLink bandwidth is exclusively yours. Choose from 4-GPU and 8-GPU configurations. Ideal for multi-week training campaigns, performance-sensitive HPC simulations, and GPU-intensive rendering where predictable performance is non-negotiable.
Kubernetes clusters with NVIDIA GPU Operator for automated driver management, device plugin deployment, and GPU-aware pod scheduling. Multi-Instance GPU (MIG) enables fine-grained GPU partitioning on A100/H100 for multi-tenant inference. GPU time-slicing for development environments. Node affinity routes training workloads to H100 nodes and inference to T4/L40S nodes. PrecisionTech deploys and manages GPU K8s clusters with Prometheus + DCGM monitoring.
Production-ready software environments: TensorFlow 2.x, PyTorch 2.x, JAX, CUDA 12.x, cuDNN, cuBLAS, NCCL, NVIDIA RAPIDS (cuDF, cuML, cuGraph), Hugging Face Transformers, DeepSpeed, Megatron-LM, vLLM. Pre-compiled for target GPU architecture (sm_80 A100, sm_90 H100). NVIDIA NGC container catalogue. Jupyter/JupyterHub with GPU kernels. MLflow experiment tracking. Kubeflow ML pipeline orchestration.
High-throughput NVMe SSD storage for training datasets, model checkpoints, and inference artefacts. Local NVMe on bare-metal nodes delivers 3,000–7,000 MB/s throughput and 1M+ IOPS. Shared NVMe-over-Fabrics (NVMe-oF) storage accessible by multiple GPU nodes simultaneously eliminates dataset duplication. Kubernetes persistent volume claims (PVCs) backed by NVMe for stateful AI containers. S3-compatible object storage for data lake integration.
PrecisionTech manages your NxtGen GPU environment 24×7 — NVIDIA DCGM monitoring (utilisation, temperature, ECC errors, NVLink throughput), GPU driver and CUDA updates, Kubernetes GPU cluster operations, storage management, InfiniBand fabric monitoring, and cost optimisation reviews. Named AI infrastructure architect, SLA-backed response times, and monthly GPU utilisation reports with right-sizing recommendations.
All GPU datacenters feature redundant power (2N), precision cooling, biometric access, 24×7 security, and fire suppression. PrecisionTech helps you select the optimal GPU datacenter for training throughput, inference latency, and data sovereignty requirements.
Sovereign GPU compute for AI startups building foundation models, fine-tuning LLMs on Indian-language datasets, training computer vision models, and deploying GenAI products. Start with A100 for training, scale to H100 clusters for larger models, and deploy inference on cost-effective T4/L40S instances. Pay-per-hour pricing with no long-term lock-in during the R&D phase.
GPU-accelerated drug discovery — molecular dynamics (GROMACS, AMBER) simulations that complete in hours vs weeks on CPUs. AlphaFold protein structure prediction, AI-driven virtual screening (AutoDock-GPU), QSAR modelling with graph neural networks, and clinical trial data analysis on RAPIDS. Proprietary compound libraries and molecular data stay in India on sovereign GPU infrastructure.
Burst rendering at scale — spin up 32+ L40S GPU instances for overnight Blender Cycles, V-Ray, and Redshift renders, then release capacity when the batch completes. NVENC hardware transcoding for 4K/8K OTT content. AI-enhanced post-production: OptiX denoising, super-resolution upscaling, AI rotoscoping. Pay only for active render hours with INR billing.
GPU compute for self-driving perception model training — LiDAR point cloud processing, 3D object detection (PointPillars, CenterPoint), multi-sensor fusion, and camera-based lane detection using large-scale labelled driving datasets. Multi-GPU H100 clusters for training on petabyte-scale sensor data. Simulation environments (CARLA, NVIDIA DRIVE Sim) run on GPU cloud for virtual test miles.
GPU-accelerated quantitative finance — Monte Carlo simulations, options pricing, portfolio optimisation, and risk modelling (VaR, CVaR) on NVIDIA RAPIDS and cuQuantFinance. AI-driven fraud detection with real-time inference on T4 GPUs. LLM-powered financial document analysis and ESG scoring. All financial data processing stays in India — RBI data localisation enforced at the infrastructure level.
Production infrastructure for GenAI SaaS platforms — fine-tune foundation models on A100/H100, serve inference endpoints on T4/L40S with vLLM or Triton Inference Server, autoscale GPU pods via Kubernetes based on request volume. Multi-tenant GPU isolation with MIG partitioning. Sovereign infrastructure for SaaS platforms serving Indian enterprise customers who mandate India data residency.
| What You Get | PrecisionTech | NxtGen Direct | Generic IT Vendor |
|---|---|---|---|
| Authorized NxtGen Cloud Partner | ✅ Yes | ✅ Yes (1st party) | ⚠️ May not be |
| ISO 9001 + ISO 27001 + CMMI L3 Certified | ✅ All three | ✅ ISO certs | ⚠️ Varies |
| GPU infrastructure architecture expertise | ✅ AI-specialised | ✅ Yes | ⚠️ Unlikely |
| Local AI support in Katherine | ✅ Yes | ⚠️ Bengaluru HQ | ⚠️ Varies |
| 24×7 managed GPU operations (DCGM) | ✅ Included | ⚠️ Premium tier | ⚠️ Extra cost |
| Multi-GPU NVLink & InfiniBand design | ✅ Yes | ✅ Yes | ❌ Unlikely |
| Multi-cloud GPU (NxtGen + AWS + Azure) | ✅ Yes | ❌ NxtGen only | ⚠️ Varies |
| AI stack configuration & optimisation | ✅ Yes | ❌ Self-service | ⚠️ Rarely |
| GPU utilisation monitoring + cost optimisation | ✅ Monthly | ❌ No | ⚠️ Rarely |
| 30-year track record in Indian IT | ✅ Since 1995 | ❌ N/A | ⚠️ Varies |
Free GPU Cloud Assessment — we evaluate your AI/ML workloads, model architecture (transformer, CNN, GNN), training dataset size, GPU memory requirements, and multi-GPU scaling needs. We recommend the optimal GPU type (A100/H100/L40S/T4), instance count, NVLink configuration, storage architecture, and deliver a GPU infrastructure blueprint with cost estimate in INR within 2 business days.
We provision your SpeedCloud AI environment — GPU instances (bare-metal or vGPU), NVLink multi-GPU interconnects, InfiniBand/RoCE networking, persistent NVMe storage, pre-built AI stacks (TensorFlow, PyTorch, CUDA), Jupyter notebooks, MLflow, and Kubernetes GPU clusters with NVIDIA GPU Operator. Performance validated with NCCL benchmarks. GPU instances live within 48–72 hours.
PrecisionTech manages your NxtGen GPU environment 24×7 — NVIDIA DCGM monitoring with Prometheus/Grafana dashboards, GPU driver and CUDA updates, Kubernetes GPU cluster operations, storage management, cost optimisation reviews, and scaling adjustments. Monthly GPU utilisation reports identify underutilised instances. Quarterly architecture reviews ensure your AI infrastructure evolves with your workloads.
Every GPU compute component PrecisionTech deploys, configures, and manages on NxtGen SpeedCloud AI for Indian enterprises
Explore the full NxtGen sovereign cloud platform — IaaS, GPU AI compute, DRaaS, BaaS, managed security, CDN, and the Infinite Datacenter hybrid cloud architecture. 99.99% uptime SLA. Indian datacenters.
Learn more →Elastic Cloud Services (ECS) — on-demand sovereign IaaS from NxtGen datacenters across India.
Learn more →NxtGen Disaster Recovery as a Service (DRaaS) — replicate and recover workloads across NxtGen datacenters in India.
Learn more →Enterprise-grade BaaS with immutable backups, granular recovery, tiered retention policies, and AES-256 encryption. Agent-based and agentless backup for VMs, databases, and files.
Learn more →Multi-layered security — NGFW, WAF, DDoS protection, EDR, SIEM, vulnerability assessment, and 24×7 SOC monitoring from NxtGen's India-based Security Operations Centre. NSX microsegmentation included.
Learn more →Compare NxtGen sovereign GPU cloud with Amazon AWS GPU instances (P5, G5, Inf2). PrecisionTech is an authorized partner for both NxtGen and AWS, helping you choose the right AI compute platform.
Learn more →Ready to deploy NVIDIA GPU compute on NxtGen SpeedCloud AI in Katherine? .
Request a Quote Free GPU AssessmentAI/ML teams in Katherine are choosing NxtGen SpeedCloud AI over hyperscaler GPU instances for structural data sovereignty, bare-metal GPU availability, InfiniBand networking for distributed training, and INR billing without egress charges. As an Authorized NxtGen Cloud Partner serving Katherine, PrecisionTech brings certified GPU infrastructure expertise — A100/H100 architecture design, multi-GPU NVLink configuration, InfiniBand fabric setup, AI stack optimisation, and Kubernetes GPU cluster management — directly to your business.
Whether you are a Katherine-based AI startup fine-tuning LLMs on proprietary data, a pharmaceutical company running molecular dynamics simulations, a VFX studio needing burst GPU rendering, an autonomous vehicle team training perception models, a fintech building AI-driven fraud detection, or an enterprise deploying a GenAI SaaS platform — PrecisionTech's Katherine GPU Cloud team delivers the right architecture, provisioning, and 24×7 managed operations on NxtGen SpeedCloud AI.
Request a Quote — GPU Cloud in KatherineAuthoritative guides on GPU cloud computing, AI/ML training infrastructure, LLM fine-tuning, and sovereign AI compute — curated by PrecisionTech's AI infrastructure architects.
A technical comparison of Ampere vs Hopper GPU architectures for large language model training — Tensor Core generations, HBM2e vs HBM3 memory bandwidth, NVLink 3 vs NVLink 4, Transformer Engine impact on mixed precision, and real-world training throughput benchmarks for 7B, 13B, and 70B parameter models.
Request the Benchmark Report →A practical guide to scaling AI training across multiple GPUs and nodes on SpeedCloud AI — data parallelism, tensor parallelism, pipeline parallelism, DeepSpeed ZeRO stages, FSDP configuration, NCCL tuning, InfiniBand topology awareness, and GPU communication profiling with NVIDIA Nsight Systems.
Download the Guide →An enterprise guide to AI data sovereignty in India — DPDPA 2023 implications for training data, model weights, and inference inputs/outputs. RBI data localisation for financial AI. Why structural sovereignty (Indian entity + Indian DCs) differs from configurable region selection on hyperscalers. India AI Mission alignment.
Get the Compliance Guide →A step-by-step guide to fine-tuning large language models on NxtGen SpeedCloud AI — full fine-tuning vs LoRA vs QLoRA trade-offs, GPU memory requirements per model size, multi-GPU training configuration with DeepSpeed, Hugging Face PEFT integration, dataset preparation, and sovereign data handling best practices.
Download the LLM Guide →A financial guide to optimising GPU cloud spend on NxtGen SpeedCloud AI — when to choose reserved vs on-demand instances, using MIG to share A100/H100 GPUs across inference workloads, DCGM-based utilisation monitoring, spot-like pricing strategies, and right-sizing recommendations based on actual GPU memory and compute usage patterns.
Get the Cost Guide →A deployment guide for building multi-tenant AI platforms on Kubernetes with NxtGen GPU infrastructure — NVIDIA GPU Operator setup, MIG-based pod isolation, GPU time-slicing for dev environments, node affinity for GPU type routing, Prometheus/DCGM monitoring, autoscaling based on inference queue depth, and cost attribution per team.
Read the K8s GPU Guide →Everything you need to know about NxtGen SpeedCloud AI GPU compute and how PrecisionTech deploys and manages GPU cloud infrastructure for businesses in Katherine.
Still have questions about NxtGen GPU Cloud in Katherine?
Talk to Our GPU Cloud ExpertFind & verify PrecisionTech across the web
Independently listed, claimed and verified on the platforms buyers trust.
