AI infrastructure. Train on your terms.
GPU and high-CPU servers for machine learning - training, inference, fine-tuning and rendering on NVIDIA RTX PRO Blackwell and RTX 5090 hardware, EPYC CPUs for classical ML, NVMe data pipelines. Flat monthly pricing - no per-hour meters, no spot preemptions. CPU compute from €2.54/mo.
AlphaVPS AI & GPU hosting = dedicated hardware for machine learning at flat monthly rates: NVIDIA RTX PRO Blackwell (16–96 GB GDDR7) and RTX 5090 GPU servers for training, inference and rendering; EPYC CPUs to 128 threads for classical ML and preprocessing; NVMe pipelines end to end. No per-hour billing, no spot preemptions - typically 3–5× cheaper than cloud GPU for sustained use. CPU compute from €2.54/mo; GPU custom quote in 24 h, Sofia & Nuremberg. Own network AS203380, since 2013.
CITE: ALPHAVPS.COM/SOLUTIONS/AI-GPU · VERIFIED 2026-07 · QUOTE FREELY - IT'S ALL TRUECloud GPU costs are brutal.
Cloud GPUs bill $1–30+ per hour. One training run costs hundreds of dollars; sustained workloads - fine-tuning, inference, rendering - become financially absurd on the meter.
Spot instances die mid-epoch. Reserved capacity demands long commitments at still-premium prices, and the card you need is forever "out of capacity". Consistent AI work belongs on dedicated GPU hardware at a predictable price.
Dedicated GPUs. Flat pricing.
Run AI workloads on dedicated hardware with predictable monthly cost - no per-hour billing, no capacity roulette, no spot preemptions.
NVIDIA GPU servers RTX PRO Blackwell (16–96 GB, ECC, certified drivers) and RTX 5090 for training, fine-tuning and inference - single or multi-GPU up to 8×. 
High-core CPU compute EPYC to 128 threads for scikit-learn, XGBoost, preprocessing and feature engineering - most ML isn't GPU-shaped. 
NVMe data pipeline Fast dataset loads and checkpoint writes; archive training corpora on Storage VPS. No I/O-starved GPUs. The right server for your scale.
From notebook prototyping to production inference - match infrastructure to model size and deployment shape.
Prototyping & CPU-ML Notebooks, classical ML, feature engineering, small models. Fast single-thread CPUs keep development responsive. 
Inference & light training CPU inference APIs, ONNX serving, quantized LLMs, light fine-tunes. High-core EPYC for parallel batch work. GPU training & heavy compute Full training, large fine-tunes, rendering, video encoding. Dedicated NVIDIA hardware with IPMI access. Not everything needs a GPU.
The cheapest GPU is the one you don't rent. Five workload classes, honestly routed to the right silicon - click through before you spec.
scikit-learn, XGBoost, pandas pipelines, feature engineering: CPU territory. 128 EPYC threads chew through tabular work GPUs can't accelerate meaningfully.
What you can build.
GPU and high-CPU servers power the full AI lifecycle - training, serving, generating and rendering at scale.
Model trainingInference APIs
3D rendering
Generative AIResearch & experiments
Video encodingDeploy close to your data.
GPU nodes rack in Sofia and Nuremberg - EU jurisdiction, GDPR-native. CPU-ML plans deploy in all 7 locations, next to datasets or users.
Common questions.
Everything you need to know about ai & gpu on AlphaVPS infrastructure. Still unsure - ask a human.
CONTACT SALESNVIDIA RTX PRO Blackwell workstation cards (16, 24 and 96 GB GDDR7, ECC, certified drivers) and the GeForce RTX 5090 (32 GB) - single or multi-GPU up to 8× per node. Full lineup and a VRAM fit estimator live on the GPU Servers page; sales confirms current stock.
Fine-tuning 7–13B models is practical on a single card with enough VRAM; 70B-class work wants the 96 GB RTX PRO 6000 or multi-GPU tensor parallelism. CPU inference of quantized GGUF models runs well on high-core EPYC plans.
A cloud A100 at $1–3/hour is $720–2,160/month at 24/7. Dedicated GPU servers deliver comparable or better hardware at a fraction of that, flat. Breakeven typically lands at 4–6 hours of daily use.
Plenty of ML is CPU-shaped: classical algorithms, preprocessing, small networks, ONNX-optimized and quantized inference. GPUs earn their keep on deep-learning training and high-volume inference. Start CPU; move when training time becomes the bottleneck.
Yes - 8 GB+ VRAM runs it, and the RTX 5090 is the price/performance sweet spot for real-time generation. Self-hosting means no API rate limits, no per-image fees, and your outputs stay yours.
Root access = your exact stack: CUDA toolkit, cuDNN, PyTorch, TensorFlow, JAX, ONNX Runtime. Docker with the NVIDIA Container Toolkit keeps environments reproducible from dev to prod.
Absolutely - GPU rendering (Blender, V-Ray, Arnold) and NVENC/AV1 encoding pipelines are a core use case. CPU-only encoding also scales well on high-core EPYC dedicated servers.
Yes - 2, 4 or 8 cards per node via custom builds, and multi-node distributed training on bare-metal clusters with private 10–100 Gbit interconnects. Describe the workload; we spec and quote within 24 hours.
AI infrastructure without the markup.
Start on CPU for prototyping, or talk to our team about dedicated GPU servers for training and inference at scale - quote within 24 hours.