AI startups
Fine-tuning a model before a launch or funding round, when the card is needed for weeks rather than years.
Service · WebJi® India
GPU VPS Hosting India
Quick answer
A GPU VPS is a KVM virtual server with one dedicated NVIDIA card — from a T4 16 GB to an A100 80 GB — plus its own vCPU, RAM and NVMe. It runs CUDA training, inference and rendering for one team. WebJi® quotes each build in INR before you pay, with 18% GST on the invoice.
For AI teams, researchers and studios who need one NVIDIA card with root access for weeks or months, not a full GPU server.
✓ In business since 2005
✓ INR + GST billing
✓ 24/7 Email Support — avg reply < 60 min
2005since
Jaipur · IN
Jaipur · IN
- In business
- 21+ yrs
- Reply time
- <60 min
- Clients
- 12K+
- Billing
- INR + GST
Last editorial update: 14 November 2026. Prices follow the dated sources on this page; checkout confirms current charges.
What is a GPU VPS?
A GPU VPS is a KVM virtual server with one physical NVIDIA card passed through to it, plus its own vCPU, RAM, NVMe disk and static IPv4. The card belongs to your server alone, so a CUDA job never waits behind another tenant, and you hold full root over SSH.
The card is the whole reason to buy it. An ordinary VPS in India starts at ₹499 a month before 18% GST and runs websites, APIs and databases well, but no amount of vCPU replaces CUDA cores for model training. When the job needs more than one card wired together, the right product is a dedicated GPU server instead.
| Attribute | CPU VPS | GPU VPS | Dedicated GPU server |
|---|---|---|---|
| Graphics card | None | One NVIDIA card, passed through | One or more cards, bare metal |
| Virtualisation | KVM | KVM | None |
| Multi-GPU (NVLink) or MIG | No | No | Yes |
| Pricing | Published, from ₹499/month | Quoted per build in INR | Quoted per build in INR |
| Typical job | Websites, APIs, mail | One model, one team, weeks to months | Large training runs, 24-hour inference |
Who needs a GPU VPS in India?
A GPU VPS suits one team running one CUDA workload that has to finish soon, where buying a card outright or renting a full server would cost more than the job is worth.
Researchers and students
Experiments whose model or dataset will not fit in a laptop GPU's memory.
Freelance ML engineers
A clean, reproducible environment per client, torn down when the contract ends.
Studios and agencies
3D rendering, Stable Diffusion batches or video transcoding done in bursts.
The five workloads that justify the card are fine-tuning and training on PyTorch or TensorFlow, inference serving for LLMs and vision APIs, diffusion image generation, RAPIDS-style data science, and rendering or encoding. Each is written against NVIDIA's CUDA stack.
Which NVIDIA card does your GPU VPS need?
VRAM decides whether a model loads at all; the card generation decides how fast it runs. FP16 weights take about 2 GB per billion parameters, so a 7B model needs roughly 14 GB before activations. That makes 16 GB an inference card and 24 GB the practical floor for fine-tuning.
| Card | Reference VRAM | Fits | Know before you choose |
|---|---|---|---|
| T4 | 16 GB GDDR6 | Inference, SDXL at 1024 px, transcoding | Turing generation: no BF16, slow for training |
| L4 | 24 GB GDDR6 | Batched inference, SDXL, transcoding | Ada generation: needs CUDA 11.8 or newer |
| A30 | 24 GB HBM2 | 7B LoRA fine-tuning, mixed inference | Lower FP16 throughput than A100 |
| L40S | 48 GB GDDR6 | 13B fine-tuning, 3D rendering | Higher power draw than A30 |
| A100 | 40 GB or 80 GB HBM2e | LLM training, large-batch inference | Multi-card NVLink needs bare metal |
| H100 | 80 GB HBM3 | Frontier-scale training | Almost always a dedicated server |
Memory figures are NVIDIA reference specifications. The exact board, driver branch and availability are confirmed in the written quote.
Quantisation changes the arithmetic: at 4-bit, 7B weights drop to around 4 GB, which brings a T4 back into play for serving. For a plain explanation of why the card beats the processor on this work, read CPU vs GPU; for a walk-through of serving a model, see running an LLM on a VPS.
How is a GPU VPS priced at WebJi®?
Every GPU VPS is quoted in INR before you pay, with 18% GST added on the invoice; there is no published GPU price grid. Five variables move together — card class, VRAM, the vCPU-to-RAM ratio, NVMe size and the operating-system licence — so a fixed table would be wrong for most buyers.
- Currency: rupees throughout, with no dollar conversion at checkout.
- Windows Server: the licence is priced inside the same quote, not added later.
- Renewal: the renewal figure is written into the quote before payment.
- CPU-only work: use the printed plans on the pricing page or a Windows VPS, which cost less when CUDA is not involved.
When is a GPU VPS the wrong choice?
Skip a GPU VPS if the job needs several linked cards, hardware partitioning, a card running flat out all day, or no CUDA at all. Saying so before the quote saves you money and a support ticket.
- NVLink multi-GPU training: a VPS exposes one card, so model-parallel runs belong on a dedicated server.
- MIG partitioning: splitting one A100 into isolated slices is a bare-metal feature.
- Round-the-clock heavy inference: a reserved or dedicated card prices better at 24-hour load.
- WordPress, Node or mail: these gain nothing from a GPU; an ordinary VPS or cloud VPS does the same work for less.
CUDA, drivers and frameworks on the GPU VPS image
The Ubuntu template boots with the NVIDIA driver, CUDA toolkit and cuDNN installed, so PyTorch and TensorFlow install against a matching build. Run nvidia-smi on first login: it prints the card, driver version and free VRAM, which proves the stack is live.
nvidia-smi
python3 -c "import torch; print(torch.cuda.is_available(), torch.version.cuda)"Pin the driver branch rather than chasing the newest toolkit. CUDA 12.x needs a 525-series Linux driver or later, and the Ada cards (L4, L40S) need a recent branch, so a PyTorch cu121 or cu124 wheel is the safer install. Docker users add the NVIDIA container toolkit, and Jupyter is reachable once its port is opened. WebJi® supports the server layer; your model code and data stay yours.
How to order a GPU VPS from WebJi®
Ordering is a short email exchange rather than a checkout, because the card and memory have to match your model.
- Email support@webji.services with the model or workload, framework and version, target VRAM, operating system and how long the job runs.
- Receive the card class, full spec (vCPU, RAM, NVMe, port) and the INR figure with GST.
- On approval the KVM server is provisioned with Ubuntu, AlmaLinux or Windows Server plus the CUDA template.
- Log in, run
nvidia-smi, then a short training or inference test to confirm throughput. - Copy data by rsync, SFTP or object storage; an existing VPS or cPanel account can be migrated free, subject to terms and approval.
After go-live the email desk covers the server layer 24/7 in Hindi and English, with an average first reply under 60 minutes. WebJi® has served 12,000+ clients online from Jaipur since 2005 and publishes a 99.9% uptime target with history on the network status page.
FAQ
Frequently asked questions
Which GPU is best for training an AI model on a VPS?
For 7B-class models at sensible batch sizes, A100 80 GB is the usual choice; L40S 48 GB covers 13B fine-tuning, and A30 24 GB handles 7B LoRA work. A T4 is built for inference and stalls on training throughput, so it is the wrong card for that job.
Can Stable Diffusion run on a 16 GB GPU VPS?
Yes. SDXL needs roughly 8–10 GB at 1024 px in FP16, so a T4 16 GB or L4 24 GB runs it comfortably. Larger batches and training a custom LoRA are what push the requirement up to 24 GB or more.
Is the GPU shared with other customers?
No. The card is passed through to a single KVM server, and that server also gets dedicated vCPU and RAM. WebJi® does not sell OpenVZ containers, so your kernel, drivers and memory are not shared with another tenant.
Can I pin my own CUDA and driver versions?
Yes. You have root over SSH, so you can hold a driver branch, install a specific CUDA toolkit or run containers with the NVIDIA container toolkit. Check that the driver meets the minimum for your CUDA release before upgrading either.
Can I move my GPU VPS to a dedicated server later?
Yes. When a project outgrows one card, the desk quotes a dedicated GPU build and the data can be copied across by rsync. Existing VPS and cPanel migrations are free, subject to terms and approval, and the old server stays live until you confirm the new one.
Last editorial update: 14 November 2026. Prices follow the dated sources on this page; checkout confirms current charges.
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