Deep learning dedicated servers
Bare-metal GPU servers for deep learning training and fine-tuning. Full GPU passthrough, dedicated CPU cores, no noisy neighbours.
Best-fit dedicated plans
These are the closest stock plans in our catalogue for Deep learning server workloads. Fully customizable — port, storage, RAM, DDoS profile.
GPU VPS · RTX PRO 4500
16 vCPU · 128 GB DDR5 · 1 TB NVMe Gen5 · RTX PRO 4500 (32 GB VRAM) full passthrough
GPU VPS · RTX PRO 6000
32 vCPU · 256 GB DDR5 · 2 TB NVMe Gen5 · RTX PRO 6000 (96 GB VRAM) full passthrough
2× / 4× / 8× GPU clusters
Custom-quoted per build — multiple Blackwell / L40S / L4 GPUs, NVLink where supported
About Deep learning server
Fine-tuning and small-to-mid deep learning training runs don't need an H100 cluster — a well-spec'd RTX PRO Blackwell box handles most LoRA / QLoRA fine-tunes, RL loops, computer vision training, and diffusion model tuning at a fraction of the cost.
On RTX PRO 6000 Blackwell (96 GB VRAM), typical workloads: full fine-tune of Llama 3 8B (batch 32, LoRA rank 64) in 4-8 hours per epoch on modest datasets; SDXL LoRA training in 20-40 min; YOLO v10 training on COCO-scale datasets in 6-12 hours. RTX PRO 4500 (32 GB) handles smaller models — 3-7B fine-tune, ViT training, up to SD 1.5 full training.
Environment: Ubuntu 22.04 + CUDA 12.4, cuDNN 9, PyTorch 2.x, JAX, HuggingFace, DeepSpeed, Accelerate, bitsandbytes, xFormers, Flash Attention pre-installed. Docker + NVIDIA Container Toolkit ready. Jupyter Lab + SSH access included.
For multi-GPU training (data / model / pipeline parallel), we build custom 2×/4×/8× GPU boxes with NVLink where supported. Send your training script and we'll spec the box.
Frequently asked questions
Can I train a full LLM from scratch on this?
For small models (< 3B params) — yes, on RTX PRO 6000. For anything larger, you'll want multi-GPU or a cluster. Custom quotes for 4×/8× builds.
Do you provide managed ML environments?
The base image is Ubuntu 22.04 + CUDA + PyTorch. We don't manage the ML stack (SageMaker-style) but engineering can help with deployment on request.
What framework versions come pre-installed?
CUDA 12.4, cuDNN 9.1, PyTorch 2.4 (stable + nightly), JAX 0.4.x, TensorFlow 2.16, HuggingFace Transformers 4.44+, DeepSpeed 0.15, vLLM. Update via pip/conda anytime.