What a GPU VPS in India gives you
A GPU VPS attaches one physical card to one virtual server, so CUDA work never queues behind another tenant. You get KVM virtualisation, full root access, a static IPv4, NVMe storage and a 1 Gbps port. Unlike a standard VPS in India, the card is the point: a CPU-only plan cannot substitute for it.
Five workloads pay for the card:
- Fine-tuning and training — LoRA or full runs on PyTorch and TensorFlow, where GPU parallelism turns day-long CPU epochs into hours.
- Inference serving — LLMs, embedding models and vision APIs, where VRAM decides which model fits.
- Diffusion and image generation — Stable Diffusion and SDXL batches.
- Data science — RAPIDS and other CUDA libraries that parallelise numeric work.
- Rendering and transcoding — 3D frames, video ladders and batch encodes.
Every item on that list is written against NVIDIA's CUDA stack, which is why the OS image, not the hypervisor, is where setup time usually goes.
GPU classes and VRAM: which card fits which job
VRAM decides what runs; the card name decides how fast. These are the NVIDIA classes WebJi quotes most often, with the reference memory size and the job each one handles. The board and availability are confirmed in the written quote.
| NVIDIA class | Reference VRAM | Job it fits | Limit to know |
|---|---|---|---|
| T4 | 16 GB GDDR6 | Inference, SDXL at 1024 px, video transcoding | Turing: no BF16, slow for training |
| L4 | 24 GB GDDR6 | Batched inference, SDXL, transcoding | Ada needs a current driver and CUDA 11.8+ |
| A30 | 24 GB HBM2 | 7B LoRA fine-tuning, mixed inference | Lower FP16 throughput than A100 |
| L40S | 48 GB GDDR6 | 13B fine-tuning, 3D rendering, SDXL batches | Higher power draw than A30 |
| A100 | 40 GB or 80 GB HBM2e | LLM training, large-batch inference | Multi-GPU needs NVLink, not a VPS |
| H100 | 80 GB HBM3 | Frontier model training | Almost always a dedicated server |
Memory figures are NVIDIA reference specifications; the exact board, driver branch and price are confirmed in the quote.
Sizing shortcut: FP16 weights need about 2 GB per billion parameters, so a 7B model needs roughly 14 GB before activations. That is why 24 GB is the practical floor for fine-tuning and 16 GB is an inference card.
What a GPU VPS in India costs and how it is quoted
WebJi does not publish a GPU price grid, because five things move together: card class, VRAM, the vCPU-to-RAM ratio, NVMe size and the OS licence. Each build is quoted in INR before payment, and 18% GST is added on the invoice. There is no dollar conversion at checkout.
Windows Server images are priced inside the same quote rather than added later. CPU-only plans are a different product and carry their own printed INR prices on the pricing page; a Windows VPS without a card is the cheaper route when CUDA is not involved.
Who a GPU VPS in India is for
A GPU VPS suits one team, one card and one job that has to run this month. It sits between a CPU VPS that cannot do the work and a dedicated GPU server that costs more than the job justifies.
- AI startups finishing a model before a launch or a funding round, where the card is needed for weeks, not years.
- Researchers and students running experiments that a laptop GPU cannot hold in memory.
- Freelance ML engineers who want a reproducible environment per client.
- Agencies doing rendering or transcoding in bursts rather than around the clock.
When a GPU VPS is the wrong buy
A GPU VPS is the wrong buy in four situations, and saying so before the quote saves a support ticket.
- NVLink multi-GPU training — a VPS exposes one card, so model-parallel training needs a dedicated GPU server.
- MIG partitioning — slicing one A100 into isolated instances is a bare-metal feature, not a virtual one.
- Sustained heavy inference — if the card runs flat out 24 hours a day, a reserved cloud GPU or a dedicated box prices better.
- No CUDA in the stack — a WordPress site, a Node API or a mail server gains nothing from a GPU, and an ordinary VPS costs less.
CUDA, drivers and framework support on a GPU VPS
The Ubuntu template boots with the NVIDIA driver, CUDA toolkit and cuDNN already installed, so PyTorch and TensorFlow install against a matching CUDA build instead of failing on a missing library. Run nvidia-smi on first login: it prints the card, the driver version and free VRAM, which is your proof the stack is live.
The driver branch matters. CUDA 12.x needs a 525-series driver or newer on Linux, and the Ada cards (L4, L40S) need a recent branch, so pinning PyTorch to a cu121 or cu124 wheel is safer than installing the newest toolkit blindly. Docker users add the NVIDIA container toolkit; Jupyter is reachable once the port is open. WebJi supports the server layer; your model code and data stay yours.
How to order and set up a GPU VPS in India
Ordering is one email, not a checkout wizard, because the spec has to match the model.
- Email support@webji.services with the model or workload, framework and version, target VRAM, OS and expected run pattern.
- The desk replies with the card class, full spec (vCPU, RAM, NVMe, port) and the INR figure including GST.
- On approval the KVM server is provisioned with your chosen image — Ubuntu, AlmaLinux or Windows Server — plus the CUDA template.
- Log in, run nvidia-smi, then a short training or inference job to confirm throughput before moving real data.
- Move data by rsync, SFTP or object storage; an existing VPS or cPanel account can be migrated free, subject to terms and approval.
Once the job is running, the 24/7 email queue covers the server layer. Reach it through the contact page or support@webji.services, in Hindi or English.
Why WebJi® for GPU VPS India
- In business since 2005, with 12,000+ clients across India and overseas served online from Jaipur.
- KVM only: no oversold OpenVZ containers, and every GPU build is quoted with its own dedicated RAM and vCPU.
- INR invoices with 18% GST, and the renewal figure sits in the quote before you pay.
- 24/7 email support in Hindi and English, average first reply under 60 minutes.
- Free migration of an existing VPS or cPanel account, subject to terms and approval.
- 99.9% uptime target, with cluster history on the public status page.
GPU VPS hosting in India: frequently asked questions
Can I get a GPU VPS in India?
Yes. WebJi quotes a GPU VPS for an Indian workload. Email support@webji.services or open a ticket and the desk sends the configuration and the INR figure before you pay. A standard web VPS does not include a GPU.
How much does a GPU VPS cost?
GPU VPS Hosting is quoted per requirement on this page, in INR, with 18% GST on the invoice. Email support@webji.services or open a ticket at https://billing.webji.in/submitticket.php?step=2&deptid=10 and the desk sends the figure before you pay. There is no dollar price converted at checkout, and a design or SEO quote is a project price — it is not a hosting renewal.
Which GPU is best for AI model training?
For training, A100 80 GB is the usual floor for 7B-class models at sensible batch sizes, L40S 48 GB covers 13B fine-tuning, and A30 24 GB covers 7B LoRA work. T4 is an inference card and stalls on training throughput.
Can I run Stable Diffusion or LLMs on a GPU VPS?
Yes. SDXL needs roughly 8–10 GB at 1024 px in FP16, so T4 16 GB or L4 24 GB runs it; a 7B LLM needs about 14 GB for FP16 inference and far less when quantised. Training rather than inference is what pushes you to A30, L40S or A100.
How much VRAM do I need for a 7B model?
About 14 GB for FP16 weights alone, so 16 GB is the minimum and 24 GB is the working figure once activations and batch memory are counted. In 4-bit quantisation the weights drop near 4 GB, which brings a T4 16 GB back into play for inference.
Is CUDA pre-installed on a GPU VPS?
The Ubuntu template ships the NVIDIA driver, CUDA toolkit and cuDNN, so PyTorch and TensorFlow install against a matching build. Run nvidia-smi on first login to confirm the card, driver version and free VRAM before you move any data across.
Do I get root access on a GPU VPS?
Yes, full root over SSH on KVM, so you can install frameworks, pin driver versions and run containers. WebJi does not resell OpenVZ containers; every GPU build is KVM with dedicated RAM and its own kernel.
When is a GPU VPS the wrong buy?
Skip it for NVLink multi-GPU training, MIG partitioning or sustained heavy inference, which need a dedicated GPU server or a reserved cloud GPU. For a CPU-only site, an ordinary VPS or a shared plan costs less and performs the same.