Rent NVIDIA RTX 4090 GPUs on Demand from $0.79/hr
24GB GDDR6X Ada Lovelace, the cheapest way to run 7B LLMs in the cloud.
You can rent an NVIDIA RTX 4090 on Spheron starting at $0.79/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 4090 ships with 24GB GDDR6X, 16,384 CUDA cores, and 4th gen Tensor Cores, giving you the best dollar-per-hour for 7B model inference, LoRA fine-tuning, Stable Diffusion image generation, and general AI prototyping. Good fit for startups, solo developers, and machine learning practitioners who don't need H100-class memory or NVLink interconnect.
Technical specifications
Pricing comparison
| Provider | Price/hr | Savings |
|---|---|---|
SpheronYour price | $0.79/hr | - |
Vast.ai | $0.30/hr | - |
RunPod (Community) | $0.34/hr | - |
RunPod (Secure) | $0.59/hr | - |
NeevCloud | $0.69/hr | - |
Need More RTX 4090 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 4090 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 4090
Pick RTX 4090 if
You're running 7B-class LLM inference, Stable Diffusion image generation, or LoRA/QLoRA fine-tuning on a budget. You want the lowest hourly GPU rate and 24GB VRAM is enough for your model. Great fit for Kaggle, prototyping, and cost-sensitive production inference.
Pick RTX 5090 instead if
You want Blackwell-generation throughput (roughly 28-50% more tokens/sec on LLMs), 32GB GDDR7, native FP4 support, or you're working with models that are slightly too big for 24GB. Small price bump, meaningful performance lift.
Pick L40S instead if
You need 48GB VRAM on a data center SKU with ECC memory, better multi-tenant isolation, and longer production lifecycle support. L40S is purpose-built for inference serving at scale.
Pick A100 or H100 instead if
You're fine-tuning or training 30B+ parameter models, need NVLink for multi-GPU, or your workload requires the HBM bandwidth and FP8 Transformer Engine of Hopper. RTX 4090 will be the bottleneck.
Ideal use cases
Cost-efficient AI development
An affordable entry point for AI and ML development. Perfect for individuals and startups building their AI projects.
Small Model Fine-Tuning
Efficiently fine-tune 7B parameter models with LoRA and QLoRA techniques. Ideal for domain-specific model adaptation at minimal cost.
AI Inference Deployment
Deploy cost-effective inference workloads at scale. Serve 7B models and smaller architectures with excellent throughput per dollar.
Creative AI & Content Generation
Run generative AI workloads affordably. One of the best GPUs for Stable Diffusion and other creative AI applications.
Performance benchmarks
Serve Llama 3.1 8B on RTX 4090 with vLLM
Spin up an OpenAI-compatible inference endpoint on a single RTX 4090. 24GB fits Llama 3.1 8B in FP16 with a 4K-8K context window depending on batch size.
# SSH into your RTX 4090 instancessh root@<instance-ip> # Install vLLM (CUDA 12.x compatible)pip install vllm # Serve Llama 3.1 8B in FP16 on a single RTX 4090vllm serve meta-llama/Meta-Llama-3.1-8B-Instruct \ --dtype float16 \ --max-model-len 4096 \ --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"}] }'Related resources
NVIDIA RTX 4090 for AI and Machine Learning: Specs, Benchmarks, and Pricing
Complete guide to RTX 4090 specs, AI benchmarks, and why it's the most affordable entry point for ML.
How to Run LLMs Locally with Ollama: GPU-Accelerated Setup Guide
Step-by-step guide to running local LLMs on RTX 4090 with Ollama for GPU-accelerated inference.
Dedicated vs Shared GPU Memory: Why VRAM Matters for AI
Understanding RTX 4090's 24GB VRAM, what fits, what doesn't, and how to optimize memory usage.
Frequently asked questions
Is the RTX 4090 enough for AI work?
Yes! The RTX 4090 is an excellent GPU for AI development, prototyping, small model fine-tuning, and inference workloads. With 24GB GDDR6X VRAM and 4th generation Tensor Cores, it offers the best price-performance ratio in our lineup. It's ideal for running 7B parameter models, Stable Diffusion, and a wide range of ML experiments without breaking the budget.
What models fit in 24GB VRAM?
24GB comfortably fits Llama 3.1 8B (FP16, ~16GB), Mistral 7B (~14GB), Stable Diffusion XL, Flux.1 Dev (Q8), and Whisper Large V3. Q4-quantized 13B models (Llama 3.3 8B, Qwen 2.5 14B) also fit. Llama 3.3 70B does not fit even at Q4 (needs ~35GB); use an A100, H100, or H200 for that class. For a full sizing guide, see our GPU requirements cheat sheet.
How does the RTX 4090 compare to the A100?
The A100 has 80GB HBM2e memory compared to the RTX 4090's 24GB GDDR6X, and 2.0 TB/s memory bandwidth vs 1.0 TB/s. A100 is the right choice once you hit memory or multi-GPU bottlenecks, or need NVLink. For anything that fits in 24GB, the RTX 4090 is typically half the hourly rate and plenty for 7B fine-tuning, inference, and image generation.
Can I use the RTX 4090 for production inference?
Yes, the RTX 4090 is well-suited for production inference for models that fit within 24GB VRAM. Multiple RTX 4090 instances can serve high traffic at a significantly lower cost than a single H100. This makes it an excellent choice for deploying 7B models, image classifiers, NLP pipelines, and chatbots in production.
Is the RTX 4090 good for Stable Diffusion?
Yes. RTX 4090 generates around 10 SDXL images per minute at 1024x1024 with base + refiner, and significantly more for SD 1.5 or lower resolutions. The combination of 82.6 TFLOPS FP32, 1 TB/s bandwidth, and 24GB VRAM makes it the default pick for self-hosted Stable Diffusion and Flux workflows.
What deep learning frameworks are supported?
All major deep learning frameworks are fully supported: PyTorch, TensorFlow, JAX, and ONNX Runtime. The RTX 4090 has full CUDA 12.x support with optimized drivers and libraries. Pre-configured Docker images are available for all frameworks, so you can start training or running inference immediately.
What's the minimum rental period?
There's no minimum rental period. Spheron charges per-minute with no contracts or commitments. The RTX 4090 is one of the lowest per-hour rates in our GPU lineup, making it ideal for short experiments, quick prototyping sessions, and extended training runs alike. You only pay for what you use.
Can I use multiple RTX 4090s together?
Yes, you can use multiple RTX 4090 instances, but note that RTX 4090 does not support NVLink for direct GPU-to-GPU communication. For training, use data parallelism across separate instances with frameworks like PyTorch DDP. For inference, deploy multiple instances behind a load balancer to handle higher throughput at a fraction of the cost of a single H100.
What regions are RTX 4090s available in?
RTX 4090 GPUs are currently available in US, Europe, and Canada regions. We're continuously expanding capacity and regions. Check our app or contact sales for specific region requirements and availability.
Do you offer support for RTX 4090 deployments?
Yes! Our team provides technical support to help you get the most out of your RTX 4090 instances. We can assist with workload optimization, cost planning, and troubleshooting issues with GPU VMs. For teams needing dedicated support, we offer enterprise plans with priority assistance.
Book a call with our team →What's the difference between dedicated and spot RTX 4090 instances?
Dedicated RTX 4090 instances are non-interruptible, run on a 99.99% SLA, and bill per-minute at the on-demand rate. Spot instances run on spare capacity at meaningfully lower rates but can be preempted when dedicated demand rises. Use spot for fault-tolerant workloads: QLoRA fine-tuning with checkpointing every 15-30 minutes, batch inference jobs, hyperparameter sweeps, and any training loop that can resume from a checkpoint. Use dedicated for production inference endpoints, customer-facing APIs, or any job where an interruption would cause data loss or an SLA breach. Both live in the same control plane, so you can mix tiers across a single project.