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.
Frequently asked questions
How does the RTX 5090 compare to the RTX 4090?
The RTX 5090 features the next-generation Blackwell architecture compared to the RTX 4090's Ada Lovelace. Key improvements include 32GB GDDR7 memory (vs 24GB GDDR6X on the 4090), approximately 2x AI performance, 5th generation Tensor Cores (vs 4th gen), and significantly higher memory bandwidth. The RTX 5090 delivers a substantial leap in AI workload performance while maintaining consumer-grade affordability.
Is the RTX 5090 good for AI training?
The RTX 5090 is excellent for training small to medium models up to approximately 13B parameters. Its 32GB GDDR7 memory handles LoRA and QLoRA fine-tuning efficiently. For larger models requiring more VRAM or higher interconnect bandwidth, consider the H100 (80GB HBM3) or A100 (80GB HBM2e) for full-scale training workloads.
What AI models can I run on 32GB VRAM?
With 32GB of GDDR7, you can comfortably run Llama 3.1 8B (FP16, ~16GB), Mistral 7B (~14GB), Qwen 2.5 14B (FP16, marginal at ~28GB, needs context limits), Stable Diffusion XL, Flux.1 Dev, and Whisper Large V3. Quantized (Q4/INT4) versions of larger models such as Qwen 2.5 32B (~20GB) also fit. Llama 3.3 70B does not fit on a single RTX 5090 even at Q4; use an H100 or H200 for that class.
How does the RTX 5090 compare to the H100?
The H100 features 80GB HBM3 memory vs the RTX 5090's 32GB GDDR7, and is 2-3x faster for large-scale training workloads. However, the RTX 5090 is approximately 2x cheaper per hour and provides excellent performance for development, inference, and fine-tuning of smaller models. Choose the RTX 5090 for cost-effective development and the H100 for production-scale training.
Can I use the RTX 5090 for video and gaming workloads?
Yes! The RTX 5090 features 4th generation RT Cores, making it excellent for real-time ray tracing, video editing, game development, and 3D rendering workloads. It is a versatile GPU that handles both AI/ML and creative professional workloads with outstanding performance.
What deep learning frameworks work with the RTX 5090?
All major deep learning frameworks are fully supported: PyTorch, TensorFlow, JAX, and ONNX Runtime. The RTX 5090 has full CUDA 12.x support, ensuring compatibility with the latest framework versions, libraries, and tools in the AI/ML ecosystem.
What's the minimum rental period?
There's no minimum rental period! Spheron charges with per-minute billing granularity. Rent an RTX 5090 for as little as a few minutes to test your workload, or keep it running as long as you need. You only pay for what you use with no long-term contracts or commitments.
Is 32GB VRAM enough for fine-tuning?
Yes, 32GB is well-suited for LoRA and QLoRA fine-tuning of models up to 13B parameters. Full fine-tuning works for 7B-class models. For full fine-tuning of larger models (30B+), consider the H100 with 80GB HBM3. The RTX 5090's fast GDDR7 memory also helps accelerate data loading during the fine-tuning process.
What regions are RTX 5090 GPUs available in?
RTX 5090 GPUs are currently available in US, Europe, and Canada regions. We're continuously expanding capacity and availability. Check our app or contact sales for specific region requirements and current availability.
Do you offer support for production deployments?
Our platform is plug-and-play for standard deployments. For 100+ GPU clusters, you get dedicated support via Slack or Discord, plus sourcing assistance. Enterprise customers get dedicated support channels and SLA guarantees.
Book a call with our team →Can I run RTX 5090 on Spot instances? What are the risks?
Yes. Spot is the interruptible tier of on-demand, priced up to 70% off the dedicated rate. Dedicated instances carry a 99.99% SLA and are non-interruptible; spot instances can be terminated when capacity is reclaimed by a dedicated workload. Key risks: job interruption during training/inference, loss of unsaved state, restart from last checkpoint. Best practices: checkpoint every 15-30 minutes, use spot for fault-tolerant or development workloads, save model weights to persistent storage, and run production serving on dedicated instances. Given the RTX 5090's already-low base price, spot makes it an exceptionally budget-friendly option for experimentation.