Cloud GPU costs are crushing AI innovation. A single H100 instance on AWS runs $30,000+ per month, while startups burn 40-60% of seed funding on compute in their first year.
We offer bare metal dedicated servers with NVIDIA's latest RTX 5090 GPU at $499/month. No markup, no surprise bills, no waiting lists.
Why the RTX 5090 Matters
The RTX 5090 is a major upgrade for AI/ML workloads:
32GB GDDR7 VRAM: Up from 24GB in the 4090. Fine-tune models up to 30B+ parameters or run larger context windows without aggressive quantization.
1,792 GB/s memory bandwidth: 78% faster than the 4090, reducing data bottlenecks.
Blackwell Architecture: Latest generation tensor cores for improved training and inference performance.
The extra VRAM means larger batch sizes and longer sequences, cutting training time by 30-40% on models like LLaMA 2 13B compared to 24GB cards.
Server Configuration
Component | Specification |
|---|---|
GPU | NVIDIA RTX 5090 (32GB GDDR7) |
CPU | AMD Ryzen 9 9950X (16-core, 5.7 GHz boost) |
RAM | 128GB DDR5-6400 (dual-channel) |
Storage | 3.84TB PCIe Gen4 NVMe |
Network | 10Gbps public port |
Location | Los Angeles datacenter |
The Ryzen 9 9950X with AVX-512 handles data preprocessing efficiently, while the enterprise NVMe provides fast dataset streaming. Full hardware access means no virtualization overhead.
Performance Benchmarks
Workload | RTX 5090 Performance |
|---|---|
Stable Diffusion XL (1024x1024) | ~2.5 seconds/image |
LLaMA 2 7B Fine-tuning | ~1,800 tokens/sec (batch 16, seq 2048) |
LLaMA 2 13B Inference | ~45 tokens/sec (FP16) |
Mixtral 8x7B Inference | ~28 tokens/sec (FP16) |
BERT Training (batch 32) | ~450 samples/sec |
YOLOv8 Training (640x640) | ~180 images/sec |
Benchmarks are approximate and vary based on specific model configurations and optimizations.
Common Use Cases
LLM Fine-Tuning: Train 7B-13B models with batch sizes of 16-32 and 2048 token sequences. 32GB VRAM eliminates the memory constraints that slow down training on smaller GPUs.
Production Inference: Deploy models up to 30B parameters for real-time serving, or run multiple smaller models simultaneously.
Image Generation: Stable Diffusion XL, Flux, and other generative models at production scale with 2-3 second generation times.
Computer Vision: Train object detection, segmentation, or classification models with the CPU handling preprocessing and GPU accelerating training.
Research: Rapid experimentation with model architectures and hyperparameters on dedicated hardware with predictable performance.
Cost Comparison
Provider | Configuration | Monthly Cost (24/7) | Cost vs Our Server |
|---|---|---|---|
Our Server | RTX 5090 (32GB) | $499 | Baseline |
AWS g6.12xlarge | 4x L4 (96GB total) | $5,145 | 10.3x more |
GCP A2 | A100 40GB | $2,664 | 5.3x more |
Azure NC24ads | A100 80GB | $3,672 | 7.4x more |
For continuous workloads, dedicated servers deliver 70-80% cost savings. Break-even point is just 3-4 hours of daily usage compared to cloud instances.
Why Bare Metal Beats Cloud
Direct hardware access eliminates virtualization overhead and noisy neighbor issues. You get:
Zero performance variability - predictable benchmarks every time
Full control over the software stack, CUDA versions, and kernel parameters
No hypervisor reserving VRAM or introducing I/O latency
Ability to install any framework or custom kernel module
Cloud instances share physical hardware, which means unpredictable performance and restricted system access. Bare metal gives you the entire machine.
Technical Details
Typical Software Stack: Ubuntu 22.04/24.04 LTS base install, root access, KVM/IPMI remote management. You manage your own software and CUDA installations.
Datacenter: Los Angeles location with sub-10ms West Coast latency, 100% power and network uptime SLA
Who This Is For
Best for: AI startups controlling costs, research teams running continuous experiments, indie developers building AI products, companies fine-tuning models for production
Not ideal for: Burst workloads running a few hours weekly (cloud spot instances may be cheaper), teams requiring specific compliance certifications, workloads requiring H100/A100 scale
Getting Started
Monthly commitment, no long-term contracts. Server provisioning within 10-30 minutes. Hardware failures are handled immediately with component replacement or server migration.
The RTX 5090 at $499/month targets the 90% of AI work that doesn't need H100 clusters: fine-tuning open source models, running production inference, iterating on research, or building AI products as a small team.
If you're spending $2,000-5,000/month on cloud GPUs, you're leaving money on the table. If you're limiting experiments because of compute costs, you're leaving innovation on the table.
Available now. No waiting lists. No surprise bills.