Model training
Train CNNs, transformers, and diffusion models on dedicated NVIDIA GPUs. Full CUDA access, NVMe for fast data loading, NCCL for multi-GPU training.
Deep Learning GPU Server
Train models on
dedicated GPUs.
NVIDIA A100, RTX 5090, and RTX 4090, full PCI passthrough, not shared.
NVMe storage for fast data loading. Independent cloud since 2008.
122,000+ users trust Cloudzy. 14-day money-back, no questions asked.
Starting at $14.47/mo · 50% off · No credit card required
~ ssh root@vps-001
connected
$ ssh root@gpu-srv-001
Welcome to Ubuntu 24.04 LTS (CUDA 12.4)
root@gpu-srv-001:~$ nvidia-smi --query-gpu=name,memory.total --format=csv
name, memory.total [MiB]
NVIDIA A100-SXM4-80GB, 81920 MiB
root@gpu-srv-001:~$ python3 -c "import torch; print(torch.cuda.is_available())"
True
root@gpu-srv-001:~$ torchrun --nproc_per_node=1 train.py --epochs 50
Epoch 1/50 | Loss: 0.4821 | LR: 1e-4
Deep Learning GPU Server at a glance
Cloudzy Deep Learning GPU Servers use NVIDIA A100, RTX 5090, and RTX 4090 GPUs with full PCI passthrough. AMD EPYC CPUs, NVMe storage, DDR5 memory, and 40 Gbps uplinks across 12 regions. CPU plans start at $2.48/mo; GPU plans available on the pricing page. Cloudzy has served 122,000+ users since 2008, rated 4.6/5 on Trustpilot. 14-day money-back on all plans.
- Starting price
- $2.48 / month
- Provisioning
- 60 seconds
- Regions
- 12 worldwide
- Uptime SLA
- 99.95%
- Money-back
- 14 days
- Founded
- 2008
Why builders pick Cloudzy
A tech-savvy favorite.
The four things buyers actually compare us on, done right.
High-spec infrastructure
Latest-gen AMD EPYC, NVMe-only storage, DDR5 memory, 40 Gbps uplinks. Single-thread leadership at every plan tier.
Risk-free trial
14-day money-back guarantee on every plan. No questions asked. No setup fees. Cancel anytime from the dashboard.
99.95% uptime SLA
Automated monitoring across 12 regions. Our last-30-day SLA is publicly tracked at status.cloudzy.com, no hiding.
24/7 human support
Live chat and ticket replies typically under 5 minutes. Engineers, not script-readers. Median resolution under 1 hour.
Use cases
Why builders choose
Cloudzy's Deep Learning GPU Server.
Fine-tuning LLMs
Fine-tune Llama, Mistral, or Gemma on A100 or RTX 5090. QLoRA on 24 GB VRAM, full fine-tune on 80 GB. NVMe handles checkpoint writes without stalling training.
Inference serving
Serve models via vLLM, TGI, or Triton on dedicated GPUs. PCI passthrough means full VRAM and full clock speeds, same performance as bare metal.
Computer vision
Object detection, segmentation, image generation. GPU-accelerated OpenCV, YOLO, Stable Diffusion. NVMe keeps training data pipelines fed without bottlenecks.
Research & prototyping
Jupyter notebooks, experiment tracking, hyperparameter sweeps. Spin up GPU servers, run experiments, tear down. 14-day money-back means low risk on new projects.
Data preprocessing
RAPIDS, cuDF, cuML. GPU-accelerated data processing for large datasets. Clean, transform, and featurize data before training. NVMe reads keep GPU utilization high.
60s
Provisioning
40 Gbps
Uplink
NVMe-only
Storage
12
Regions
99.95%
Uptime SLA
14 days
Money-back
Global network
12 regions. Four continents.
One click away.
Drop your Deep Learning GPU Server as close to your users as physics allows. Median P50 latency under 10 ms in North America and Europe.
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Pricing
Pay for what you use. That's it.
Hourly, monthly, or yearly. No egress fees. No commitments. Currently 50% off all plans.
Most popular
4 GB DDR5
Entry GPU workloads · fine-tuning prep
$14.47 /mo
$28.95/mo −50%
Deploy now
14-day money-back
- 2 vCPU @ EPYC
- 120 GB NVMe
- 5 TB · 40 Gbps
- Dedicated IPv4 + IPv6
- Root SSH · KVM
8 GB DDR5
Training data pipelines · preprocessing
$26.475 /mo
$52.95/mo −50%
Deploy now
14-day money-back
- 4 vCPU @ EPYC
- 240 GB NVMe
- 7 TB · 40 Gbps
- Dedicated IPv4 + IPv6
- Root SSH · KVM
16 GB DDR5
Multi-GPU coordination · model serving
$49.98 /mo
$99.95/mo −50%
Deploy now
14-day money-back
- 8 vCPU @ EPYC
- 350 GB NVMe
- 10 TB · 40 Gbps
- Dedicated IPv4 + IPv6
- Root SSH · KVM
32 GB DDR5
Large-scale training · distributed compute
$109.975 /mo
$219.95/mo −50%
Deploy now
14-day money-back
- 12 vCPU @ EPYC
- 750 GB NVMe
- 12 TB · 40 Gbps
- Dedicated IPv4 + IPv6
- Root SSH · KVM
FAQ — Deep Learning GPU Server
Common questions, straight answers.
Which GPUs are available?
NVIDIA A100 (1x, 2x, 4x), RTX 5090 (1x, 2x), and RTX 4090 (1x, 2x, 4x). All use PCI passthrough, the GPU is dedicated to your VM, not shared. Full VRAM, full clock speeds, full CUDA access. See the pricing page for current GPU plan details and availability.
Are the GPUs shared or dedicated?
Dedicated. PCI passthrough gives your VM exclusive access to the physical GPU. CUDA, NVENC, NCCL all behave exactly like bare metal. No time-sharing, no MIG partitioning, no virtualization overhead on the GPU itself.
What CUDA version is available?
GPU plans ship with pre-configured CUDA images, currently CUDA 12.x on Ubuntu LTS. You can install any CUDA version you need since you have full root access. PyTorch, TensorFlow, JAX, and other frameworks install via pip or conda as usual.
How much VRAM do I need for deep learning?
Depends on your model. Fine-tuning a 7B LLM with QLoRA fits in 24 GB. Full fine-tune of a 7B model needs 40+ GB. Training from scratch on large models or running fp16 70B inference needs 80 GB (A100). Match GPU plan to your model's memory footprint.
Can I do multi-GPU training?
Yes. Plans with 2x or 4x GPUs support NCCL for distributed training. PyTorch DDP, DeepSpeed, FSDP, all work as expected. NVMe storage handles checkpoint saves without stalling the training loop.
Is there a money-back guarantee on GPU plans?
Yes, 14 days, full refund, no questions asked. Run your actual training job, benchmark your inference pipeline. If the GPU server doesn't meet your needs, you get your money back.
How fast is provisioning?
60 seconds from payment confirmation. GPU plans boot with a pre-configured CUDA image, nvidia-smi returns immediately. Install your framework and start training in minutes, not hours.
Can I use these for inference in production?
Yes. Dedicated GPU, 99.95% uptime SLA, dedicated IPv4. Run vLLM, Triton, or your own inference server behind a load balancer. 40 Gbps network handles high-throughput inference traffic.
Do I also get CPU and storage?
Yes. GPU plans include AMD EPYC CPUs (12-64 vCPU depending on plan), DDR5 RAM (48-768 GB), and NVMe storage (500 GB to 6 TB). The CPU handles data preprocessing while the GPU trains. NVMe keeps data loading fast.
How does pricing compare to cloud GPU providers?
Cloudzy GPU plans use dedicated hardware with no time-sharing overhead. Pricing is listed on the pricing page, transparent monthly and annual rates with no hidden compute-hour charges. 14-day money-back lets you test before committing.
Dedicated GPUs, ready now.
Deploy in 60 seconds.
No credit card required · 14-day money-back guarantee · Cancel anytime