GPU Compute Infrastructure
High-performance GPU instances optimized for deep learning, model inference, and embeddings.
What this is
Dedicated virtual and bare-metal compute nodes outfitted with enterprise NVIDIA GPUs, engineered to deliver the memory bandwidth and compute throughput needed for AI inference and fine-tuning.
How it works
- 1Select required GPU memory footprint (VRAM) and PCIe interconnect tier.
- 2Instances provisioned with pre-installed NVIDIA drivers, CUDA toolkits, and container runtimes.
- 3High-throughput NVMe scratch disks provide fast local caching for model weights.
- 4Direct SSH and Jupyter notebook access for engineering teams.
What's included
| GPU Hardware | Enterprise NVIDIA Tensor Core GPUs |
| Host Memory | 64 GB - 256 GB ECC System RAM |
| Model Storage | Ultra-fast NVMe local caching arrays |
| Drivers | Pre-configured NVIDIA driver and CUDA environment |
Suitability assessment
✓ When this makes sense
- •Companies self-hosting open-source LLMs (Llama, Mistral) for private inference.
- •High-throughput vector embedding generation and computer vision processing.
- •Model fine-tuning where cloud hyperscaler on-demand pricing is prohibitive.
✕ When it doesn't
- •Standard web applications that require simple CPU compute with no matrix math.
Technical details & architecture deep-dive▾
PCIe Gen4/Gen5 x16 host interconnects delivering direct memory access, coupled with local NVMe caches capable of streaming multi-gigabyte model weights into VRAM in seconds.
Frequently asked questions
Yes. Instances are deployed with stable NVIDIA drivers and container runtime support (nvidia-container-toolkit) ready for Docker execution.