GPU
GPUs, on tap
From RTX A4000 to NVIDIA H100 — priced by the hour or by the month.
- NVIDIA A100 · H100 · H200
- NVLink + InfiniBand fabric
- Full root · IPMI KVM
- Free DDoS mitigation
- Hourly or monthly billing
- EU & Asia data centres
RTX A-series, A100, H100, H200 & RTX 6000 PRO.
Up to 400 Gb/s interconnect for multi-GPU training.
GDPR, NIS2 and SOC 2-ready facilities.
Always-on mitigation baked into every port.
Currency
Choose billing period
Spark
Entry-level GPU for inference, small training runs & rendering.
৳34,875/mo · save 20%
৳669,600 billed every 24 months
Choose plan- 1× NVIDIA RTX A4000 · 16 GB GDDR6
- Intel Xeon E-2388G · 8c / 16t
- 128 GB DDR4 ECC · 2 TB NVMe
- 1 Gbps unmetered · 1 dedicated IPv4
- CUDA · cuDNN · PyTorch · TensorFlow
- Ubuntu / Debian / Rocky / Windows
- 24/7 monitoring · free DDoS shield
Surge
Balanced GPU for mid-scale training, VDI & video pipelines.
৳58,125/mo · save 20%
৳1,116,000 billed every 24 months
Choose plan- 1× NVIDIA RTX A5000 · 24 GB GDDR6
- Dual Xeon Silver 4314 · 32c / 64t
- 256 GB DDR4 ECC · 2 TB NVMe + 4 TB SATA
- 10 Gbps port · 5 dedicated IPv4
- Docker · NVIDIA Container Toolkit
- IPMI KVM · full root access
- Priority ML/HPC engineer support
Fusion
Serious 48 GB accelerator for large models & 3D rendering farms.
৳96,875/mo · save 20%
৳1,860,000 billed every 24 months
Choose plan- 1× NVIDIA RTX A6000 · 48 GB GDDR6
- Dual Xeon Gold 6338 · 64c / 128t
- 512 GB DDR4 ECC · 4 TB NVMe RAID
- 10 Gbps port · 5 IPv4 · /64 IPv6
- NVLink-ready · vGPU / MIG optional
- Blender · Redshift · Octane · Unreal
- 99.9% uptime SLA
Reactor
A100 & H100 clusters for frontier AI training and inference.
Tailored to your requirement — pay only for what you need.
- 1–8× A100 80 GB · H100 · H200 · RTX 6000 PRO
- AMD EPYC up to 192 cores · 2 TB RAM
- NVLink · NVSwitch · InfiniBand 200/400 Gb
- Petabyte NVMe / parallel filesystem
- GDPR · NIS2 · SOC 2 · HIPAA-ready DCs
- Private VLAN · VPC peering · BYO IP
- Dedicated MLOps & 24/7 white-glove ops
GPU availability varies by region. 1 USD = 125 BDT · 1 EUR = 155 BDT. Hourly billing available on Surge and above.
Workloads
Built for anything GPU-hungry.
From a single-card inference box to an 8× H100 training rig — the same bare-metal control plane, the same predictable pricing.
PyTorch, TensorFlow, JAX, vLLM, TGI — CUDA + cuDNN preinstalled.
Blender, Redshift, Octane, Arnold. Farm-out heavy scenes with OptiX.
NVENC / NVDEC pipelines for streaming, transcoding and live events.
Molecular dynamics, CFD, Monte Carlo — MPI + NCCL over InfiniBand.
vGPU / MIG partitioning to serve dozens of GPU desktops per card.
RAPIDS, cuDF and Spark-on-GPU for petabyte-scale pipelines.
Pick your card
Every NVIDIA class, one contract.
Not sure which GPU fits? Match the workload to the memory tier — our engineers will right-size the rest (CPU, RAM, storage, network) before provisioning.
- Single-GPU or 2× / 4× / 8× nodes
- NVLink bridges on A6000 / A100 / H100
- InfiniBand fabric for multi-node clusters
- Bring your own IP · private VLAN
Under the hood
Every layer of the stack, ready on boot.
We install the driver stack, the container runtime and the frameworks you'd otherwise burn a week on — so nvidia-smi works the moment you SSH in.
- Ubuntu 22.04 · Debian · Rocky Linux · Windows Server
- NVIDIA driver · CUDA 12 · cuDNN · NCCL
- Docker + NVIDIA Container Toolkit
- PyTorch · TensorFlow · JAX · Hugging Face
- vLLM · TGI · Ollama · TensorRT-LLM
- Blender · Redshift · Octane · Unreal Engine
- Prometheus + Grafana GPU dashboards
- IPMI KVM · PXE reinstall · rescue mode
Zero setup
SSH in. Everything works.
CUDA, cuDNN, the NVIDIA Container Toolkit and your chosen framework are already there. Pull an image, mount your data, start training.
- Full root · custom kernels
- IPMI KVM · rescue mode
- Free reinstall on demand
- Private image registry
- Snapshots & block storage
- Bring-your-own OS ISO
- Weekly patch windows
- 24/7 hands-on-hardware
$ nvidia-smi --query-gpu=name,memory.total --format=csv
name, memory.total [MiB]
NVIDIA H100 80GB HBM3, 81559 MiB
$ docker run --gpus all -it \
nvcr.io/nvidia/pytorch:24.05-py3 \
python -c "import torch; \
print(torch.cuda.get_device_name(0))"
NVIDIA H100 80GB HBM3
$ python train.py --precision bf16 \
--model llama-3-70b --gpus 8
[00:00] compiling kernels ... ok
[00:14] step 1/10000 loss=6.42 tok/s=118432Why bare-metal GPU
Dedicated silicon beats time-sliced cloud.
No hypervisor tax, no noisy neighbours — the full card is yours 24/7.
Choose kernel, drivers, storage layout and network topology.
Unmetered ports on Spark; huge inclusive transfer on higher tiers.
EU / Asia DCs with GDPR, NIS2 and SOC 2 — your data, your jurisdiction.
InfiniBand + NCCL topology for multi-node distributed training.
Flat monthly pricing — the same H100 for a fraction of hyperscaler rates.
Frequently asked questions
How quickly can a GPU server be provisioned?+
RTX-class servers (A4000 / A5000 / A6000) usually deploy in 30–90 minutes. A100 / H100 clusters are hand-configured and typically ready inside one business day.
Can I rent hourly instead of monthly?+
Yes — Surge, Fusion and Reactor tiers support hourly billing on request. Spark is monthly-only to keep pricing aggressive.
Do you offer multi-GPU nodes and clusters?+
Absolutely. Ask for 2×, 4× or 8× GPU nodes on A6000 / A100 / H100 / H200, with NVLink or NVSwitch inside a node and InfiniBand between nodes.
Which frameworks are preinstalled?+
The image ships with CUDA 12, cuDNN, NCCL, the NVIDIA Container Toolkit, PyTorch, TensorFlow and JAX. Rendering images add Blender, Redshift and Octane on request.
Where are the datacenters?+
Primary GPU capacity is in EU (GDPR / NIS2 compliant). We also provision in Singapore, India and the US on request — good for latency-sensitive inference in Asia.
Can I bring my own OS or hypervisor?+
Yes. Attach a custom ISO via IPMI or ask us to PXE-boot your image. Proxmox, ESXi and custom Linux distros are all supported.