AI Frameworks
TensorFlow, PyTorch, and Keras on CUDA-tuned AI GPU server hosts—train faster with modular, production-ready pipelines.
AI GPU Server Hosting
Database Mart AI server hosting gives you dedicated NVIDIA GPUs to train local LLMs, deploy models, and run inference—full root on every AI GPU server when you would rather self-host than buy third-party API tokens.
Pricing
AI GPU Server Plans
GPU dedicated server tiers for AI server hosting on our stack—compare live AI server price and total AI server cost before you AI server buy in the plan comparison table. View All GPU Plans →
| Plans | GPU | CPU | Memory | Disk | Bandwidth | GPU Memory | NVLink | Price | |
|---|---|---|---|---|---|---|---|---|---|
Professional GPU VPS - RTX Pro 2000 | RTX Pro 2000 | 16 CPU Cores | 28GB RAM | 240GB SSD | 300Mbps Unmetered | 16 GB GDDR7 | -- | $116.35/mo | Order Now |
| Professional Dedicated GPU Server - RTX 2060 | RTX 2060 | 16-Core Dual E5-2660 | 128GB RAM | 120GB SSD + 960GB SSD | 100Mbps Unmetered | 6 GB GDDR6 | -- | $159.00/mo$0.22/hour | Order Now |
| Advanced Dedicated GPU Server - RTX 2060 | RTX 2060 | 40-Core Dual Gold 6148 | 128GB RAM | 120GB SSD + 960GB SSD | 100Mbps Unmetered | 6 GB GDDR6 | -- | $119.50/mo | Order Now |
| Advanced GPU VPS - RTX Pro 4000 | RTX Pro 4000 | 24 CPU Cores | 56GB RAM | 320GB SSD | 500Mbps Unmetered | 24 GB GDDR7 | -- | $189.00/mo | Order Now |
| Advanced Dedicated GPU Server - V100 | V100 | 24-Core Dual E5-2690v3 | 128GB RAM | 240GB SSD+2TB SSD | 100Mbps Unmetered | 16 GB HBM2 | -- | $229.00/mo | Order Now |
| Enterprise Dedicated GPU Server - RTX 4090 | RTX 4090 | 36-Core Dual E5-2697v4 | 256GB RAM | 240GB SSD+2TB NVMe+8TB SATA | 100Mbps Unmetered | 24 GB GDDR6X | -- | $409.00/mo | Order Now |
| Enterprise Dedicated GPU Server - RTX A6000 | RTX A6000 | 36-Core Dual E5-2697v4 | 256GB RAM | 240GB SSD+2TB NVMe+8TB SATA | 100Mbps Unmetered | 48 GB GDDR6 | -- | $409.00/mo | Order Now |
| Advanced GPU VPS - RTX Pro 5000 | RTX Pro 5000 | 24 CPU Cores | 56GB RAM | 320GB SSD | 500Mbps Unmetered | 48 GB GDDR7 | -- | $359.00/mo | Order Now |
| Enterprise Dedicated GPU Server - A40 | A40 | 36-Core Dual E5-2697v4 | 256GB RAM | 240GB SSD+2TB NVMe+8TB SATA | 100Mbps Unmetered | 48 GB GDDR6 | -- | $439.00/mo | Order Now |
| Enterprise Dedicated GPU Server - A100 | A100 | 36-Core Dual E5-2697v4 | 256GB RAM | 240GB SSD+2TB NVMe+8TB SATA | 100Mbps Unmetered | 40 GB HBM2 | -- | $639.00/mo | Order Now |
| Enterprise Dedicated GPU Server - A100(80GB) | A100(80GB) | 36-Core Dual E5-2697v4 | 256GB RAM | 240GB SSD+2TB NVMe+8TB SATA | 100Mbps Unmetered | 80 GB HBM2e | -- | $1559.00/mo | Order Now |
| Enterprise GPU VPS - RTX Pro 6000 | RTX Pro 6000 | 32 CPU Cores | 84GB RAM | 400GB SSD | 1000Mbps Unmetered | 96 GB GDDR7 | -- | $649.00/mo | Order Now |
| Enterprise Dedicated GPU Server - H100 | H100 | 36-Core Dual E5-2697v4 | 256GB RAM | 240GB SSD+2TB NVMe+8TB SATA | 100Mbps Unmetered | 80 GB HBM2e | -- | $2099.00/mo | Order Now |
| Enterprise Multi-GPU Dedicated Server - 4xA100 | 4 x A100 | 44-core Dual E5-2699v4 | 512GB RAM | 240GB SSD+4TB NVMe+16TB SATA | 1000Mbps Unmetered | 40 GB HBM2 | 6xNVLink | $1899.00/mo | Order Now |
| Enterprise Multi-GPU Dedicated Server - 2xRTX 4090 | 2 x RTX 4090 | 36-Core Dual E5-2697v4 | 256GB RAM | 240GB SSD+2TB NVMe+8TB SATA | 1000Mbps Unmetered | 24 GB GDDR6X | -- | $729.00/mo | Order Now |
| Enterprise Multi-GPU Dedicated Server - 2xRTX 5090 | 2 x RTX 5090 | 44-core Dual E5-2699v4 | 256GB RAM | 240GB SSD+2TB NVMe+8TB SATA | 1000Mbps Unmetered | 32 GB GDDR7 | -- | $859.00/mo | Order Now |
Use cases
Use Cases of AI Server Hosting
Self-hosted GPU for deep learning and generative AI—not managed LLM APIs or workloads outside AI/ML.
| In scope | Outside scope |
|---|---|
| Renting an AI GPU server to train local large models, deploy LLMs on your hardware, run batch or online inference, fine-tune weights, and tune serving parameters—anything you need dedicated GPU time for when you do not want to depend on vendor API tokens. | Customers who only want a hosted AI API and prefer to purchase OpenAI, Claude, or similar API tokens instead of running models themselves. General business hosting, game servers, or other non-AI / non-deep-learning workloads that do not need NVIDIA training or inference GPUs. |
AI Solutions for DL / LLM / AIGC
Modular stacks on one AI server hosting platform—frameworks, LLM tooling, vector memory, and creative AI on dedicated AI GPU server hardware.
LLM Frameworks & Tools
Ollama, vLLM, Hugging Face Transformers, and LangChain for self-hosted LLMs, RAG agents, and high-throughput inference on AI server hosting.
Vector Database
ChromaDB, Milvus, and Qdrant for embeddings, semantic search, and RAG memory beside LLM workloads on the same AI GPU server.
AI Image Generation
Stable Diffusion, ComfyUI, and Fooocus on your AI GPU server—custom AIGC workflows without SaaS VRAM caps.
AI Code Generation
Code Llama, CodeGemma, and Codestral for completion and polyglot generation on mid-tier AI server hosting lines.
AI Audio & OCR
Whisper, ChatTTS, Coqui TTS, and PaddleOCR for speech and document pipelines on cost-aware AI hosting tiers.
Sizing
AI Server Hosting Workload & Metrics
Match GPU memory and server type to training or inference before you compare AI server price on each AI GPU server tier.
| Workload | Typical GPUs | Server type | Notes |
|---|---|---|---|
| LLM fine-tuning | A100 40/80GB, H100 80GB | Dedicated AI GPU server | High VRAM; check AI server cost for multi-GPU nodes |
| LLM inference / self-hosted API | RTX 4090, RTX 5090, A6000 | GPU dedicated or VPS | vLLM or TGI; scale across AI server hosting nodes |
| Stable Diffusion / ComfyUI | RTX 4090, RTX A5000+ | GPU dedicated | 24GB+ VRAM on a single AI GPU server |
| Vector RAG pipeline | RTX 4060–4090 + CPU RAM | GPU dedicated | Embed on GPU; Milvus/Qdrant on same host |
| Research & notebooks | RTX A4000, RTX Pro series | GPU dedicated / VPS | Jupyter + SSH on AI training server instances |
Key NVIDIA GPU Performance Metrics
VRAM, bandwidth, and tensor cores—how to read specs on an NVIDIA AI server before you commit.
| Metric | Description | Why it matters | Recommended use |
|---|---|---|---|
| VRAM | GPU memory (e.g. 24GB, 80GB) | Max model size, batch size, resolution | LLM training, large vision models |
| Memory bandwidth | GB/s between cores and memory | Dataset throughput for big tensors | 3D/vision, high-res batches |
| CUDA cores | Parallel FP32 units | Raw compute for general training | Simulation, mixed-precision jobs |
| TFLOPS (FP16/FP8) | Ops per second at lower precision | Faster training & quantized inference | Transformers, CNNs at scale |
| Tensor cores | Matrix multiply accelerators | GEMM-heavy deep learning | LLMs, modern CNNs |
| NVLink / PCIe | GPU-to-GPU bandwidth | Distributed & model-parallel training | Multi-GPU A100 / H100 nodes |
| TDP & cooling | Power under load | Datacenter power planning | Long-running training clusters |
| Driver / CUDA stack | cuDNN, NCCL compatibility | Framework version support | Verify PyTorch / TensorFlow builds |
Common Open-Source AI & Deep Learning Frameworks
Pre-install or configure after provisioning—CUDA-matched builds on AI server hosting orders.
| Framework | Language | Primary use | Key features | Best for |
|---|---|---|---|---|
| PyTorch | Python, C++ | Research, training, inference | Dynamic graphs, intuitive debugging, active community | Researchers, CV/NLP, startups |
| TensorFlow | Python, C++ | Training & deployment | TF Lite, TF Serving, cross-platform graphs | Enterprise production pipelines |
| JAX | Python | Research, performance | High-performance autodiff, NumPy-like API | Performance-focused modeling |
| Keras | Python | Prototyping | High-level API on TensorFlow | Beginners, fast experiments |
| Transformers (Hugging Face) | Python | Pretrained NLP / LLMs | BERT, GPT, LLaMA zoo, fine-tuning helpers | LLM inference & RAG on AI GPU server tiers |
| ONNX | Model format | Interoperability | Export across PyTorch, TensorFlow, runtimes | Deployment & framework switching |
| Detectron2 | Python | Computer vision | Detection, segmentation from Meta | CV researchers & practitioners |
| Fastai | Python | Education & rapid trials | Clean API over PyTorch | Students, educators, prototyping |
Open-Source LLMs You Can Run on GPU
Pick VRAM on the right AI GPU server for model size—4090, A6000, and A100 class hosts for inference and fine-tuning.
DeepSeek-R1
Qwen 2.5
LLaMA 3.x
Gemma 3
Mistral 7B
Phi-4
Plan fit
AI GPU Server Plan Comparison
Eight recommended tiers—workload fit, typical AI server price drivers, and limits. Confirm live AI server cost in the console before checkout.
| Plan | Key params (typical) | VRAM / GPU | Best for | Training vs inference | Why this tier | Limits |
|---|---|---|---|---|---|---|
| Entry GPU · GTX 1650 / T1000 class | 8 cores · 32 GB · entry GPU | 4–8 GB | Learning, small models, OCR | Inference-first | Lowest AI server price to try dedicated GPU tiers | Not for LLM training |
| RTX 4060 / 3060 Ti class | 12 cores · 48 GB · Ada/Ampere | 8–12 GB | RAG embed, small LLM serve | Light inference | Budget AI server hosting for prototypes | VRAM caps model size |
| RTX 4090 class | 16 cores · 64 GB · Ada | 24 GB GDDR6X | Single-GPU fine-tune, SD, mid LLM | Both; inference sweet spot | Popular AI GPU server for teams that AI server buy first | Single-GPU training scale |
| RTX 5090 class | 20 cores · 96 GB · Blackwell | 32 GB GDDR7 | Large single-GPU training | Training & inference | Successor perf per AI server cost dollar | GeForce driver policies |
| RTX A5000 / A6000 | 24 cores · 128 GB · Ampere Pro | 24–48 GB ECC | Vision, NLP, workstation LLM | Training-heavy | Stable NVIDIA AI server for 24/7 jobs | Below datacenter A100 throughput |
| A100 40GB | 32+ cores · 256 GB · Ampere DC | 40 GB HBM2 | Multi-GPU research | Training | Datacenter training standard on CUDA | VRAM vs 80GB variant |
| A100 80GB / H100 80GB | 48+ cores · 512 GB · Hopper/Ampere | 80 GB HBM | Large LLM train & deploy | Training at scale | Flagship AI server hosting for foundation models | Premium AI server price tier |
| Multi-GPU A100 / H100 node | NVLink · high core · multi-TB RAM | 2–8× 80GB | Foundation model teams | Distributed training | Maximum throughput on AI GPU server clusters | Lead time & power class |
Top NVIDIA GPUs for PyTorch & TensorFlow
Reference specs when you map workloads to hardware—confirm availability in the console.
| GPU | Arch | VRAM | FP16 class | Tensor cores | Best use case | Notes |
|---|---|---|---|---|---|---|
| H100 | Hopper | 80GB HBM3 | ~200+ TFLOPS | 4th-gen | LLM training, multi-GPU | Flagship AI GPU server for large models |
| A100 80GB | Ampere | 80GB HBM2e | ~78 TFLOPS | 3rd-gen | Large-model training | Common datacenter choice |
| A100 40GB | Ampere | 40GB HBM2 | ~78 TFLOPS | 3rd-gen | Multi-GPU research | Lower VRAM vs 80GB plan |
| RTX 5090 | Blackwell | 32GB GDDR7 | ~160+ TFLOPS | 5th-gen | Single-GPU training | Successor to 4090 class |
| RTX 4090 | Ada | 24GB GDDR6X | ~83 TFLOPS | 4th-gen | R&D, vision, NLP | Strong perf per dollar on AI server hosting |
| RTX A6000 | Ampere | 48GB ECC | ~39 TFLOPS | 3rd-gen | Large models, workstations | ECC VRAM for stability |
| RTX A5000 | Ampere | 24GB ECC | ~27 TFLOPS | 3rd-gen | Vision / NLP training | Balanced pro GPU |
| V100 | Volta | 16GB HBM2 | ~15.7 TFLOPS | 2nd-gen | Legacy DL workloads | Entry AI GPU server for experiments |
GPU selection tips
- Prioritize VRAM and tensor-core throughput for training on a dedicated AI GPU server before adding multi-GPU nodes.
- For inference at scale, weigh INT8/FP8 TFLOPS, memory efficiency, and power on AI server hosting tiers.
- Multi-GPU setups need NVLink (where available) and NCCL-friendly networking on NVIDIA GPU server nodes.
- List AI server price per tier beside projected monthly AI server cost before you add a second GPU—VRAM and power class move both numbers.
FAQ
FAQs of AI Server Hosting
Self-hosted GPU vs API tokens—how AI server price and AI server cost compare to token spend—and deployment on Database Mart AI GPU server plans.
Teams that train or deploy local LLMs, tune inference, or must keep data on their own hardware—AI server hosting fits when recurring API token spend exceeds dedicated GPU AI server cost.
PyTorch and TensorFlow jobs, LLM fine-tuning, Stable Diffusion, and RAG stacks—any workload that needs CUDA on an AI training server with full root.
Yes—we provide NVIDIA GPUs only, from GeForce RTX through datacenter H100 and A100. Each NVIDIA AI server line ships with CUDA-ready drivers for your AI server hosting stack; we do not offer AMD or other GPU vendors on these plans.
No—AI hosting here means you rent GPU servers and install frameworks yourself. Managed token APIs from third-party vendors are outside the in-scope boundary on this page.
25+ options including H100, A100, RTX 5090, RTX 4090, and RTX A6000—select live inventory when you order AI server hosting.
GPU VPS often provisions in about 5 minutes; dedicated AI GPU server orders typically in about 20–40 minutes after payment on AI server hosting.
Deploy Your AI GPU Server Today
Order GPU plans for local LLM training and deployment—AI server hosting with full root on NVIDIA AI GPU server hardware.