Rent RTX 3090 in the Cloud – Deploy in Seconds on RunPod

Instant access to RTX 3090 GPUs—ideal for AI model training and high-resolution gaming—with hourly pricing, global availability, and fast deployment. The NVIDIA GeForce RTX 3090, equipped with 24 GB GDDR6X VRAM and 10,496 CUDA cores, excels at handling large AI models and high-resolution graphics, offering unparalleled computational power and memory capacity. Rent on RunPod to scale your resources flexibly, avoid hefty upfront costs, and ensure secure, efficient performance for your most demanding tasks, leveraging RunPod's capabilities.

Why Choose the RTX 3090

The NVIDIA GeForce RTX 3090 offers a compelling mix of performance and accessibility, ideal for AI developers, data scientists, and graphics professionals. Its high memory capacity and powerful computational capabilities make it a versatile tool for tackling complex tasks among the top GPUs for AI.

• High Memory Capacity
  With 24GB of GDDR6X VRAM, the RTX 3090 can handle larger AI models and datasets, facilitating faster training and data processing, and supporting high-resolution asset rendering. Learn more about the GDDR6X memory advantages.
• Strong Computational Performance
  Equipped with 10,496 CUDA cores and 328 Tensor Cores, the RTX 3090 excels in parallel processing and AI acceleration, significantly boosting throughput in tasks like image classification and natural language processing.
• Cost-Effective Flexibility
  Renting the RTX 3090 via RunPod eliminates the need for large upfront investments, allowing users to pay for only the GPU time they use, while offering the flexibility to scale resources quickly and efficiently through our cost-effective GPU solutions.
• NVLink Support for Scalability
  The RTX 3090 supports NVLink, enabling multi-GPU configurations that effectively double memory capacity and throughput, perfect for large-scale AI model training and distributed computing tasks. For more information on NVLink vs PCIe, see our FAQ.

Specifications

For more detailed GPU performance metrics, refer to our GPU benchmarks.

FAQ

RunPod provides on-demand access to RTX 3090 GPUs through their cloud platform. You select the number of GPUs needed, choose from pre-configured software environments, and start using the GPU power within minutes. The system offers flexibility to scale resources up or down as your requirements change.

Pricing varies depending on demand and availability. On competitive platforms, hourly rates typically range from $0.50 to $2.00 per hour. Some providers offer discounts for longer-term rentals or bulk usage. Check RunPod's pricing details and RTX 3090 pricing insights for the most up-to-date rates.

You can usually access your rented RTX 3090 within minutes of initiating the rental. The exact time depends on current demand and availability, but the process is designed to be quick and seamless.

Yes, you can rent multiple RTX 3090s together on RunPod. This setup particularly benefits tasks that leverage multi-GPU architectures, such as training large AI models or running parallel computations. The RTX 3090 supports NVLink, allowing for efficient communication between multiple GPUs when properly configured.

To maximize RTX 3090 performance:

1. Ensure proper cooling: The RTX 3090 generates significant heat. Proper cooling maintains optimal performance, especially during long compute tasks.
2. Use the latest drivers: Keep NVIDIA drivers updated for the latest optimizations and bug fixes.
3. Optimize your software stack: Use GPU-accelerated libraries and frameworks designed for the RTX 3090's architecture.
4. Monitor GPU utilization: Tools like NVIDIA-SMI or MSI Afterburner help ensure your GPU is fully utilized.
5. Consider undervolting: Some users report improved performance and thermal management through careful undervolting.

RunPod typically offers various pre-configured environments tailored for different use cases, including:

• TensorFlow
• PyTorch
• CUDA and cuDNN
• Jupyter Notebooks
• Popular machine learning libraries (scikit-learn, XGBoost, etc.)

The exact list of pre-configured environments may vary, so check RunPod's current offerings for up-to-date information.

RunPod provides built-in monitoring tools that track GPU usage, memory consumption, and other vital metrics in real-time. You can also use NVIDIA's native tools like nvidia-smi or third-party monitoring software compatible with the RTX 3090.

RunPod implements several security measures:

1. Encryption: Data in transit is protected using TLS/SSL protocols, and data at rest is encrypted.
2. Isolation: Each user's environment is isolated to prevent unauthorized access between tenants.
3. Access Controls: Strong authentication protocols and role-based access controls protect resources.
4. Regular Audits: Security practices undergo regular audits and updates to address new threats.

For more information, refer to RunPod's security measures.

After your rental ends, RunPod employs secure data wiping procedures to ensure your data remains inaccessible to subsequent users. As a best practice, manually remove any sensitive data before ending your session and keep important data backed up elsewhere.

When using rented GPUs:

1. Use strong, unique passwords and enable two-factor authentication when available
2. Encrypt sensitive data before uploading it to the rented GPU
3. Regularly update any software or frameworks you install
4. Monitor access logs and report suspicious activity
5. Remove sensitive data before ending your rental period

The RTX 3090 excels at training large language models due to its 24GB of VRAM and high computational power. A recent benchmark showed that a single RTX 3090 could serve the Llama 3.1 8B model to over 100 concurrent users at FP16 precision, delivering approximately 12.88 tokens per second. Compared to alternatives:

• It outperforms most consumer-grade GPUs in memory capacity and compute power
• It offers a more cost-effective solution than professional-grade GPUs like the A100 or H100 SXM for many workloads
• While newer GPUs like the RTX 4090 offer improved performance, as shown in GPU comparison benchmarks, the 3090 remains popular due to its balance of performance and cost

For detailed comparisons in AI model training performance, see our GPU comparison pages.

To learn more about RunPod's LLM capabilities, visit our AI FAQ.

The RTX 3090 offers several data processing advantages:

1. High Memory Capacity: 24GB of VRAM processes large datasets that wouldn't fit in less powerful GPUs.
2. Fast Memory Bandwidth: 936 GB/s memory bandwidth quickly moves data in and out of GPU memory.
3. Parallel Processing: 10,496 CUDA cores enable massive parallelization of data tasks, speeding up operations like feature engineering and data augmentation.
4. Support for Mixed Precision: Accelerates certain computations while maintaining accuracy.

These features make the RTX 3090 particularly effective for large-scale data preprocessing, feature extraction in computer vision, and processing time-series data.

The RTX 3090 enhances game development through:

1. High-Resolution Rendering: Real-time rendering at 4K and 8K resolutions enables creation and testing of highly detailed graphics.
2. Ray Tracing: Dedicated RT cores provide excellent ray tracing performance for implementing and testing advanced lighting and reflection effects.
3. DLSS Testing: Developers can test and optimize DLSS implementation, increasingly important in modern games.
4. VR Development: High performance makes it ideal for developing and testing virtual reality applications requiring high frame rates and low latency.
5. Asset Creation: Processing power significantly speeds up rendering times for high-poly models, textures, and other game assets.
6. Simultaneous Tool Operation: High VRAM and processing power allow running resource-intensive development tools alongside games, improving workflow efficiency.

While the RTX 3090 offers significant advantages, other GPUs such as the RTX 6000 rental are also available for developers needing even more power.

Renting through platforms like RunPod offers several advantages over traditional cloud services:

1. Cost: Often more cost-effective for shorter-term or intermittent usage
2. Flexibility: Easier scaling without long-term commitments
3. Specialized Hardware: Access to consumer-grade GPUs like the RTX 3090, AMD GPU rental options, and high-end GPUs such as the H200, not always available on all cloud platforms
4. Simplicity: More straightforward setup process focused on GPU-accelerated workloads.

However, large cloud providers may offer more comprehensive ecosystems, additional services, and potentially better support for enterprise-scale operations.

Renting makes more sense in these scenarios:

1. Short-term projects: Avoid high upfront costs for limited-time GPU needs
2. Irregular usage: Pay only for what you use when workloads don't require constant GPU access
3. Scaling needs: Easily adjust GPU resources as requirements fluctuate
4. Testing and experimentation: Try different GPU configurations or workflows before committing to a purchase
5. Avoiding depreciation: Access the latest hardware without worrying about your GPU becoming outdated.

Buying might be more cost-effective with consistent, long-term GPU needs where you can fully utilize the hardware.