Full GPU power. No virtualisation tax.
Bare-metal NVIDIA H100, A100, and RTX 4090 servers with PCIe 5.0, NVLink, and NVMe local storage. Zero GPU partitioning, zero vGPU overhead — every CUDA core is yours.
Everything AI/ML teams need at the hardware level
No virtualisation layer between your model and the GPU. Just bare metal, CUDA, and full bandwidth.
Choose from single-GPU workstations (RTX 4090) up to 8-GPU HGX clusters (H100 SXM5). All GPUs are current-generation with full NVLink support for multi-GPU training.
Unlike cloud vGPU instances, bare-metal gives you 100% of every GPU's compute units, memory bandwidth, and NVLink lanes. No MIG slicing, no VRAM reduction.
H100 SXM5 ships with 80 GB HBM3 per GPU at 3.2 TB/s memory bandwidth. A100 80 GB configs support the largest models and full-precision training runs without offloading.
PCIe 5.0 host connectivity and local NVMe drives eliminate dataset loading bottlenecks. Run training directly from local NVMe at 14 GB/s without relying on network-attached storage.
Servers ship with NVIDIA drivers, CUDA toolkit, and NCCL pre-installed. PyTorch, TensorFlow, JAX, and vLLM inference stacks are available as optional pre-configured OS images.
NVIDIA DCGM (Data Center GPU Manager) provides GPU health telemetry, ECC error tracking, and thermal monitoring out-of-band. Get alerts before a GPU failure affects your training run.
GPU Dedicated configurations
From single RTX 4090 workstations to 8× H100 HGX clusters. All bare-metal, no vGPU, no MIG.
What teams run on GPU Dedicated
Train 70B+ parameter models without vGPU bottlenecks
Bare-metal GPUs give you full NVLink bandwidth between GPUs — critical for tensor parallelism across multiple H100s. Fine-tune LLaMA 3, Mistral, or custom models at full hardware speed with no cloud markup.
Serve thousands of inference requests per second with vLLM
Low-latency inference for GPT-4-class models requires dedicated GPU memory. Our A100-80 and H100 configs support continuous batching with vLLM, TensorRT-LLM, and TGI — serving production traffic without cold starts.
Offline rendering and physics simulation on RTX 4090
Blender, Cinema 4D, Unreal Engine rendering, and NVIDIA PhysX / Omniverse simulations run 3–5× faster on dedicated RTX 4090 hardware vs cloud GPU VMs. Full RT Core and Tensor Core access without sharing.
Frequently asked questions
Train faster on bare-metal GPUs.
From a single RTX 4090 to an 8× H100 cluster — provisioned in hours, priced by the month or hour. No vGPU tax, no MIG, no surprises.