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GPU Product

Rent NVIDIA RTX 4090 GPUs

High-performance GPUs for AI and deep learning workloads

Technical Specifications

ArchitectureNVIDIA Ada Lovelace
Memory Size24 GB GDDR6X
Memory Bandwidth1010 GB/s
Ray Tracing Cores128
Tensor Cores512
NVIDIA RTX 4090
Rental Options

RTX 4090 Rental Options

The NVIDIA GeForce RTX 4090 is available on-demand with pay-as-you-go pricing. Get capacity quickly without upfront hardware costs, and scale as your training needs grow.

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Performance

Key performance metrics

Deep Learning Acceleration

Experience up to 1.9 times higher training throughput compared to the RTX 3090, significantly reducing model training durations.

Enhanced Multi-GPU Scaling

Achieve near-linear performance gains with multi-GPU configurations, optimizing resource utilization for large-scale computations.

Advanced Memory Bandwidth

1,010 GB/s bandwidth enables 3x faster data transfer than previous generations, reducing bottlenecks in large AI model training and complex data processing workloads.

Use Cases

Popular use cases

AI Research

Train large language models and neural networks for cutting-edge AI research.

Machine Learning

Accelerated training and inference for production-scale machine learning models.

Data Science

Fast processing of large datasets and complex statistical calculations.

Rendering

High-speed 3D rendering and real-time visualization for content pipelines.

Comparison

RTX 4090 vs RTX 5090 Benchmark

The most common 4090 vs 5090 benchmark question for AI workloads is throughput-per-dollar. In our vLLM single-GPU test on Qwen3-Coder-30B, the RTX 5090 produced about 2× the tokens per second of the RTX 4090. The 4090 still wins on raw hourly cost — but the 5090 wins on cost per million tokens. Full numbers and multi-GPU configurations live on the RTX 5090 page.

ConfigurationThroughputHourly CostCost / 1M Tokens
1× RTX 40902,259 tok/s$0.39$0.048
1× RTX 50904,570 tok/s$0.65$0.040

Source: CloudRift LLM inference benchmark. The RTX 4090 remains the right pick when hourly budget is the hard constraint or when 24 GB of VRAM is enough for the target model. For higher concurrency or larger context windows, the RTX 5090 is more cost-efficient overall. See all GPU benchmarks →

RTX 4090 FAQ

Common Questions About the RTX 4090

Ada Lovelace architecture with 24 GB VRAM GDDR6X Memory. Built for intensive parallel compute and AI workloads.
TensorFlow, PyTorch, and JAX run on CUDA with cuDNN support. Container and VM images are available for quick setup.
Yes. It delivers strong training throughput and fast inference while the 24 GB VRAM helps prevent out of memory issues on many models.
Yes. Multi GPU nodes are available for data parallel or model parallel training in many regions.
LLM fine tuning, diffusion and generative media, vector search, and large scale data processing. It also handles high end 3D rendering and visualization.
In our 4090 vs 5090 benchmark on Qwen3-Coder-30B, the RTX 5090 hit 4,570 tokens/s versus 2,259 tokens/s on the RTX 4090 — about 2× the throughput. The RTX 4090 is the better pick when hourly budget is fixed or when 24 GB of VRAM is enough for the model. The RTX 5090 wins on cost per million tokens for high-concurrency inference.
Open the Console, create a new instance, choose container or VM, choose RTX 4090, select GPU count and launch. For longer term reservations, contact us.
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