Rent NVIDIA RTX 5090 GPUs on Demand from $0.86/hr
32GB GDDR7 Blackwell, deployed in under 2 minutes.
You can rent an NVIDIA RTX 5090 on Spheron starting at $0.86/hr per GPU per hour on dedicated (99.99% SLA, non-interruptible), with spot instances cheaper still. Per-minute billing, no contracts, deployed in under 2 minutes across data center partners in multiple regions. The RTX 5090 packs 32GB of GDDR7 memory and 5th gen Tensor Cores, making it the best price-to-performance choice for LoRA/QLoRA fine-tuning of 7B-13B models, Stable Diffusion XL inference, local LLM serving with Ollama or vLLM, and general AI development work. Launch a container with your CUDA/PyTorch image, SSH in, and start training in minutes.
Technical specifications
Pricing comparison
| Provider | Price/hr | Savings |
|---|---|---|
SpheronYour price | $0.86/hr | - |
CloudRift | $0.65/hr | - |
NeevCloud | $0.69/hr | - |
RunPod (Community) | $0.69/hr | - |
RunPod (Secure) | $0.99/hr | 1.2x more expensive |
Need More RTX 5090 Than What's Listed?
Reserved Capacity
Commit to a duration, lock in availability and better rates
Custom Clusters
8 to 512+ GPUs, specific hardware, InfiniBand configs on request
Supplier Matchmaking
Spheron sources from its certified data center network, negotiates pricing, handles setup
Need more RTX 5090 capacity? Tell us your requirements and we'll source it from our certified data center network.
Typical turnaround: 24–48 hours
When to pick the RTX 5090
Pick RTX 5090 if
Your workload is LoRA/QLoRA fine-tuning on 7B-13B models, Stable Diffusion XL or Flux inference, or local LLM serving where 32GB VRAM is plenty. You want the cheapest Blackwell-generation GPU with 5th gen Tensor Cores and aren't bottlenecked by multi-GPU interconnect.
Pick RTX 4090 instead if
You need the absolute lowest hourly rate and 24GB VRAM is enough for your model. Your workload doesn't benefit from Blackwell's 2x AI throughput or the bandwidth jump from GDDR6X to GDDR7.
Pick RTX PRO 6000 instead if
You need 48GB or 96GB VRAM on Blackwell silicon to serve 30B+ quantized models on a single GPU, or you want pro-tier drivers and ECC memory for production workloads.
Pick H100 instead if
You're training or fine-tuning 30B+ parameter models end-to-end, need HBM3 bandwidth and NVLink/InfiniBand for multi-GPU, or your workload requires the Hopper FP8 Transformer Engine.
Ideal use cases
AI Prototyping & Development
Rapidly iterate on AI models at low cost, making the RTX 5090 ideal for development workflows and early-stage experimentation.
Small Model Fine-Tuning
Perform LoRA and QLoRA fine-tuning of models up to 13B parameters with 32GB of fast GDDR7 memory.
Cost-Effective Inference
Deploy smaller models at minimal cost for production inference workloads that demand high throughput at a budget-friendly price.
AI Education & Research
Affordable GPU access for learning, research, and open-source contributions without the overhead of expensive data center GPUs.
Performance benchmarks
Serve Llama 3.1 8B on RTX 5090 with vLLM
Spin up an OpenAI-compatible inference endpoint on a single RTX 5090. The 32GB GDDR7 fits Llama 3.1 8B in FP16 with room for an 8K context window.
# SSH into your RTX 5090 instancessh root@<instance-ip> # Install vLLM (CUDA 12.x compatible)pip install vllm # Serve Llama 3.1 8B in FP16 on a single RTX 5090vllm serve meta-llama/Meta-Llama-3.1-8B-Instruct \ --dtype float16 \ --max-model-len 8192 \ --gpu-memory-utilization 0.9 \ --port 8000 # Test the OpenAI-compatible endpointcurl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "meta-llama/Meta-Llama-3.1-8B-Instruct", "messages": [{"role": "user", "content": "Hello"}] }'RTX 5090 vs alternatives
Related resources
Dedicated vs Shared GPU Memory: Why VRAM Matters for AI
Understanding RTX 5090's 32GB GDDR7 advantage over the 4090's 24GB for AI model loading.
How to Run LLMs Locally with Ollama: GPU-Accelerated Setup Guide
Run local LLMs on RTX 5090 with Ollama, Blackwell architecture makes inference faster than ever.
GPU Requirements Cheat Sheet 2026
Find out which AI models fit on 32GB VRAM and which need more, practical sizing for RTX 5090.