Instant access to RTX 4090 GPUs—ideal for AI model training and data rendering—with hourly pricing, global availability, and fast deployment. The NVIDIA GeForce RTX 4090 offers unparalleled performance with 16,384 CUDA cores and 24GB GDDR6X VRAM, making it perfect for handling large datasets and complex models. Rent this powerhouse on RunPod to rapidly accelerate your workflows with seamless integration and flexible scaling.

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Why Choose RTX 4090

The RTX 4090 stands as a computational powerhouse that brings exceptional advantages for developers, startups, and researchers tackling demanding workloads.

Benefits

• Generous VRAM
  With 24GB of GDDR6X memory, the RTX 4090 handles large AI models and high-resolution datasets with ease. This supports bigger mini-batch sizes for faster convergence in training sessions.
• State-of-the-art Architecture
  With 16,384 CUDA cores and 4th generation Tensor Cores, the RTX 4090 excels at mixed-precision training (FP16, BFLOAT16). This dramatically boosts throughput without significant accuracy drops, delivering real-time inference speeds perfect for rapid experimentation.
• Raw AI Horsepower
  The RTX 4090 delivers AI processing power measured in trillions of operations per second (TOPS), surpassing previous-generation consumer GPUs by a significant margin, as documented on NVIDIA's AI on RTX site.
• DLSS AI Upscaling
  Deep Learning Super Sampling technology enhances both visual rendering and certain deep learning workflows, boosting output performance by up to 200%.
• Broad Framework Support
  The RTX 4090 works seamlessly with TensorFlow, PyTorch, and Hugging Face. For guidance on running PyTorch on RTX 4090, see our detailed FAQ. Regular NVIDIA driver updates ensure ongoing improvements in speed and stability.

For an overview of other GPUs suitable for AI, check out our guide on the best GPUs for AI models.

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Specifications

FeatureValueGPU ArchitectureAda Lovelace (AD102)CUDA Cores16,384Tensor Cores4th GenerationRT Cores3rd GenerationBase Clock2,235 MHzBoost ClockUp to 2,640 MHz (OC Mode)Memory24 GB GDDR6XMemory Interface384-bitMemory Bandwidth1,008 GB/sPCIe InterfacePCIe 4.0FP32 ComputeUp to 82.6 TFLOPSFP16 Tensor ComputeUp to 330.3 TFLOPSRay TracingUp to 191 RT TFLOPSAI/Deep Learning4th-gen Tensor Cores with FP8 supportVRAM24GB GDDR6XPower ConsumptionTypical board power is around 450WOutputs3x DisplayPort 1.4a, 2x HDMI 2.1a

If you're comparing GPUs, you might also be interested in the power consumption of GPUs like the NVIDIA H100.

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FAQ

How do different GPU rental providers compare for RTX 4090 rentals?

Each provider offers distinct advantages: RunPod features Docker containers, API/CLI support, multi-region availability, and flexible scaling. Vast.ai uses a bidding system, often offering the lowest prices but requires more hands-on management. Lambda Labs provides transparent pricing popular among ML researchers and startups. Paperspace is user-friendly with managed environments, ideal for education and prototyping. Genesis Cloud focuses on high-end enterprise options with EU compliance. For budget-conscious projects, Vast.ai typically offers the lowest RTX 4090 rental prices. However, if you value deployment simplicity and predictable pricing, RunPod and Lambda Labs stand out as leading choices. In addition to RTX 4090 rentals, providers like RunPod also offer other powerful GPU options, such as A100 GPU rental and AMD GPU rental, for a variety of demanding workloads. For a comprehensive comparison of serverless GPU platforms, see our article on top serverless GPU platforms.

What pricing models are available?

Most providers offer several pricing options: Hourly billing is ideal for short-term or experimental workloads. Reserved/Subscription offers discounted rates for longer commitments. Custom/Bidding models, like those on Vast.ai, use market-driven pricing with real-time bidding. RTX 4090 rentals on Vast.ai start from around $0.40/hour (median), while a 4x RTX 4090 configuration might cost approximately $1,350/month for a dedicated server. Considering that purchasing a new NVIDIA RTX 4090 can be a significant investment (refer to NVIDIA RTX 4090 pricing), renting provides a cost-effective alternative for many users. Understanding the billing cycle, minimum usage requirements, and potential additional costs for storage or data transfer before committing is crucial. Providers like RunPod offer cost-effective GPU cloud computing solutions tailored for AI teams. For detailed pricing structures, refer to GPU instance pricing.

Is there enough supply of RTX 4090 GPUs available for rent?

Supply fluctuates based on demand. Some providers maintain waitlists for specific configurations, especially multi-GPU setups like 4x or 8x RTX 4090s. Check real-time availability or contact providers directly for current status.

Can I rent multiple RTX 4090 GPUs in a single instance?

Yes, many providers offer multi-GPU configurations, though availability may be limited for high-demand setups. Consider alternatives or joining a waitlist for specific multi-GPU arrangements if needed.

How does the RTX 4090 perform for AI and deep learning tasks?

The RTX 4090 excels in AI and deep learning workloads. Its 16,384 CUDA cores, 24GB GDDR6X memory, and 4th generation Tensor Cores deliver significant performance gains over previous generations. Training large vision or language models on a single RTX 4090 can be up to 2x faster than on an RTX 3090; for more details, see our RTX 3090 vs RTX 4090 comparison. For a comparative performance context between the RTX 4090 and other GPUs like the H100 SXM, refer to our RTX 4090 vs H100 SXM comparison. To discover which models you can run on an NVIDIA RTX 4090, check out AI models on RTX 4090.

What software environments and frameworks are supported?

Most providers support popular AI frameworks including TensorFlow, PyTorch, CUDA and cuDNN, Docker containers, and Jupyter notebooks. Verify specific version compatibility and pre-installed options with your chosen provider.

How are RTX 4090 rentals typically billed?

Billing practices vary: Hourly rates are common for on-demand usage. Daily or monthly rates are available for longer-term rentals. Usage-based billing means some providers charge based on actual GPU utilization. Understand the billing cycle, minimum usage requirements, and potential additional costs for storage or data transfer before committing.

Are there ways to optimize costs for RTX 4090 rentals?

Consider these cost-saving strategies: Use interruptible instances for non-critical workloads. Take advantage of reserved pricing for long-term projects. Optimize code to reduce unnecessary GPU time. Use spot instances or bidding systems on platforms like Vast.ai for potential savings.

How is data security handled on rented RTX 4090 instances?

Reputable providers implement several security measures: Isolated environments (virtualization or containers), data wiping between users, encryption for data at rest and in transit, and compliance with regulations like GDPR or HIPAA (where applicable). Review the provider's security policies and address any specific concerns before proceeding. For example, see RunPod's security measures for detailed information.

What kind of support can I expect when renting an RTX 4090?

Support offerings vary but may include: Documentation and setup guides, community forums, email support, live chat or phone support (often for premium tiers), and onboarding assistance for new users. Check support options and response times offered by your chosen provider.

Are the rented RTX 4090 GPUs new or refurbished?

This varies by provider. Some use new hardware, while others might use refurbished or repurposed data center GPUs. Reports from Chinese data centers indicate some are refurbishing and selling RTX 4090s due to overcapacity. Ask your provider about hardware condition and any potential performance implications.

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