AI & GPU
Server Hosting.

Our GPU server offerings change based on availability. We work with NVIDIA GPUs suitable for ML training and inference. Contact our sales team for current GPU models, VRAM configurations, and pricing. For CPU-based AI workloads, our standard VPS and dedicated servers with AMD EPYC processors provide excellent performance without GPU requirements.

Yes, with the right hardware. Fine-tuning smaller models (7B-13B parameters) is practical on single-GPU servers with sufficient VRAM. Full training of large models requires multi-GPU setups - contact us for custom configurations. For CPU-based inference of quantized models (GGUF/GGML), our high-core VPS handles many open-source LLMs.

Significantly cheaper for sustained usage. Cloud GPU instances (A100) cost $1-3/hour on-demand - that's $720-$2,160/month for 24/7 use. Dedicated GPU servers offer the same or better hardware at a fraction of that cost. The breakeven point is typically 4-6 hours of daily usage.

Many ML workloads run well on CPUs: classical ML (scikit-learn, XGBoost), data preprocessing, feature engineering, small neural networks, and inference of optimized models (ONNX). GPUs accelerate deep learning training (CNNs, transformers, diffusion models) and large-scale inference. Start with CPU; move to GPU when training time becomes the bottleneck. For CPU-heavy data processing and ETL pipelines, our standard VPS plans are a cost-effective starting point.

Yes. Stable Diffusion runs on GPUs with 8GB+ VRAM. On CPU, generation is slower but still functional for low-volume use. Our GPU servers handle real-time generation for production workloads. Self-hosting means no API rate limits, no per-image costs, and complete control over models and outputs.

Full root access means you install exactly the CUDA toolkit version and framework versions your project needs. PyTorch, TensorFlow, JAX, ONNX Runtime - all work natively. Use Docker with NVIDIA Container Toolkit for reproducible environments across development and production.

Absolutely. GPU-accelerated rendering (Blender, V-Ray, Arnold) and encoding (FFmpeg with NVENC/VAAPI) run on our GPU servers. For CPU-based encoding, AMD EPYC's high core count handles parallel encoding jobs efficiently. Batch rendering farms are a popular use case.

Yes, custom multi-GPU configurations are available through our custom dedicated servers. Contact our sales team to discuss your requirements - model size, training framework, VRAM needs - and we'll recommend the right configuration. For multi-node distributed training, explore our Bare Metal Clusters.