Offshore GPU Dedicated Servers — NVIDIA RTX & A-Series

Cloud GPU pricing makes sense for bursty workloads — spin up an A100 instance for a two-hour training run and release it. For anything with steady utilization (daily inference services, 24/7 transcoding, continuous rendering queues) the math flips hard. An AWS p3.2xlarge (one V100) at on-demand pricing runs about $3.06 per hour, or roughly $2,200 per month if left running. An AnubizHost GPU server with an RTX A4000 (roughly equivalent inference throughput on many workloads) runs $199/mo — a 10x cost difference for comparable capability on typical workloads.

Beyond cost, dedicated GPU hardware avoids the noisy-neighbor issues common on shared GPU slices in cloud environments. When a GPU is fully yours, you get consistent memory bandwidth, consistent PCIe throughput, and predictable thermals. For latency-sensitive inference serving (chatbots, recommendation systems, fraud detection) this predictability is worth more than the raw FLOPS difference between cards.

We do not virtualize the GPU. You get PCIe passthrough of the whole card to your operating system with no hypervisor overhead. This also means you can drive multiple CUDA streams, use NVLink if your configuration includes it, and operate the card exactly as if it were in a workstation under your desk.

Our GPU lineup focuses on workstation-class NVIDIA cards that balance capability, cost, and availability. The RTX A4000 (16 GB GDDR6, Ampere architecture) is our entry GPU and handles inference for quantized LLMs up to about 13 billion parameters, video transcoding at professional quality, and 3D rendering for Blender/Cinema4D workflows. The RTX A5000 (24 GB GDDR6) extends inference capability to 30-billion-parameter models and doubles transcoding throughput.

The RTX 4090 (24 GB GDDR6X, Ada Lovelace) is our performance option — newer architecture, significantly higher FP16 throughput, and excellent for fine-tuning smaller models or running Stable Diffusion / Flux image pipelines at high throughput. For customers who need enterprise-class capacity, we offer RTX A6000 (48 GB) and NVIDIA L40S configurations on request.

GPU add-on pricing stacks on top of our standard server tiers. A popular starting configuration is the Performance base ($129/mo) with an RTX A4000 add-on ($100/mo) for a total of $199/mo ready-to-go inference server.

The full GPU dedicated server starts at $199/mo with a 16-core AMD Ryzen 9 5950X or Intel Xeon E-2388G, 64 GB DDR4 RAM, 2x 1.92 TB NVMe, 1 Gbps unmetered, and a single RTX A4000 GPU. The Performance GPU tier at $269/mo upgrades to AMD EPYC 7313P with 128 GB ECC RAM and an RTX A5000. The Pro GPU tier at $349/mo delivers EPYC 7443P, 256 GB ECC RAM, 4x NVMe, 10 Gbps unmetered, and an RTX 4090.

Multi-GPU configurations (2x or 4x GPU in a single chassis) are available on request starting around $599/mo depending on GPU choice. These configurations are platform-dependent (we match the motherboard to the GPU count) and generally deliver inside one week.

GPU server provisioning runs 48 to 72 hours because we hand-assemble the GPU configuration and run extended hardware validation (GPU stress tests, ECC memory checks, thermal soak tests) before delivery. This is a deliberate quality gate — a flaky GPU discovered at month six is expensive for both sides.

On delivery we pre-install a CUDA-capable base environment: Ubuntu 24.04 LTS with the NVIDIA driver (current stable), CUDA Toolkit, cuDNN, and nvidia-container-toolkit for Docker GPU passthrough. Popular AI stacks — PyTorch, TensorFlow, vLLM, Text Generation Inference (TGI), Ollama, ComfyUI for Stable Diffusion — can be pre-installed on request.

For transcoding customers we pre-install FFmpeg with NVENC/NVDEC support compiled in, tested against common codec targets (h264, HEVC, AV1 for the RTX 4090). Rendering customers get Blender with GPU render backend pre-configured, or we can install their preferred rendering pipeline.

Finding a provider that will sell you a GPU dedicated server at reasonable prices without demanding extensive KYC is surprisingly hard. The major cloud providers (AWS, GCP, Azure) require full KYC and charge cloud GPU rates that are 5-10x what dedicated should cost. Smaller GPU-focused providers often require identity verification and run US-based infrastructure with no offshore privacy benefits.

AnubizHost offers GPU dedicated hardware in offshore jurisdictions (Netherlands, Finland, Iceland primarily, where GPU-capable colocation is available) with the same no-KYC, crypto-accepted policy that applies to our non-GPU infrastructure. For AI operators running on sensitive data — private model fine-tunes, proprietary training datasets, inference for clients with confidentiality requirements — offshore + dedicated GPU is the right combination.

Our pricing is transparent and honest, our hardware is real (not a VM with vGPU passthrough pretending to be bare metal), and our support team includes engineers who have actually deployed PyTorch and vLLM in production. If you need GPU capacity for a steady inference workload or a rendering pipeline, AnubizHost is a practical alternative to the hyperscaler cloud-GPU treadmill.