Rent NVIDIA RTX 4090 GPUs on Demand from $0.79/hr

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.