Powering the next generation of AI & high-performance computing.
Engineered for large-scale AI training, deep learning, and high-performance workloads, delivering unprecedented compute power and efficiency.
Why rent the RTX 4090 instead of buying?
Consumer price, professional capability
The RTX 4090 delivers 82.6 TFLOPS FP32 and 24 GB of GDDR6X — more raw compute than many data center cards from the previous generation, at a fraction of the cost of an H100. Runpod's on-demand pricing lets you access RTX 4090 instances from $0.34/hr, with no hardware purchase, no depreciation, and no idle costs between projects.
FP8 inference on Ada Lovelace
Ada Lovelace introduced native FP8 Tensor Core support, giving the 4090 up to 660.6 sparse TOPS for quantized inference workloads. That means production-speed inference on models up to ~13B parameters — at consumer GPU pricing. For teams running high-throughput inference rather than heavy training, the 4090 delivers exceptional value per dollar.
Deploy in seconds, scale without limits
Provision an RTX 4090 pod in seconds. Run multi-card configurations, switch GPU types, or shut everything down when a project wraps. Runpod handles power, cooling, and maintenance so you don't have to.
Use Cases
Popular use cases.
Designed for demanding workloads —learn if this GPU fits your needs.
Technical Specs
Ready for your most demanding workloads.
Essential technical specifications to help you choose the right GPU for your workload.
| Specification | Details | Great for... |
|---|---|---|
| Architecture | NVIDIA Ada Lovelace (AD102) | Workloads requiring 4th-gen Tensor Cores, 3rd-gen RT Cores, and native FP8 support |
| Manufacturing Process | TSMC 4N | — |
| Transistors | 76.3 billion | — |
| Die Size | 608 mm² | — |
| Form Factor | FHFL, dual-slot PCIe | Deploying in standard PCIe workstation and server slots |
| CUDA Cores | 16,384 | Parallelizing large AI training, rendering, and simulation workloads |
| Tensor Cores | 512 (4th generation) | Mixed-precision training and inference with TF32, BF16, FP16, FP8, and INT8 support |
| RT Cores | 128 (3rd generation) | Real-time ray tracing for rendering, VFX, and interactive visualization |
| GPU Memory | 24 GB GDDR6X | Running mid-size LLMs, large batch sizes, and high-resolution datasets without CPU offloading |
| Clock Speeds | Base 2,235 / Boost 2,520 MHz | Sustained high-frequency compute across long training and inference runs |
| Power Consumption | ~450 W TDP | High-throughput workloads where absolute performance outweighs power efficiency |
| FP64 Performance | ~1.3 TFLOPS | — |
| FP32 Performance | 82.6 TFLOPS | Standard-precision training, simulation, and rendering compute |
| TF32 Tensor Core | 82.6 TFLOPS (165.2 sparse) | Accelerated training with near-FP32 accuracy at 2× the throughput |
| BF16 Tensor Core | 165.2 TFLOPS (330.3 sparse) | Large model training with the numeric stability of FP32 |
| FP8 Tensor Core | 330.3 TFLOPS (660.6 sparse) | Maximum inference throughput with quantized models — Ada Lovelace native |
| INT8 Tensor Core | 660.6 TOPS (1,321.2 sparse) | Production inference at scale with quantized models |
"The Runpod team has clearly prioritized the developer experience to create an elegant solution that enables individuals to rapidly develop custom AI apps or integrations while also paving the way for organizations to truly deliver on the promise of AI."
Amjad Masad
"Runpod is the only place I can deploy high-end GPU models instantly—no sales calls, no rate limits, no nonsense."
Daniel Chang
“The main value proposition for us was the flexibility Runpod offered. We were able to scale up effortlessly to meet the demand at launch.”
Josh Payne
“Runpod helped us scale the part of our platform that drives creation. That’s what fuels the rest—image generation, sharing, remixing. It starts with training.”
Matty Shimura
Comparison
Powerful GPUs. Globally available. Reliability you can trust.
30+ GPUs, 31 regions, instant scale. Fine-tune or go full Skynet—we’ve got you.
FAQs
Questions? Answers.
What are the current rental rates for an RTX 4090 on Runpod?
Rates vary by instance type and availability. For the most current pricing, see the Runpod pricing page.
How is billing handled for RTX 4090 rentals?
Runpod bills by the second — you pay only for active compute time, with no minimum commitment. On-demand and spot instance pricing are both available. For a full breakdown of pricing options, see the Runpod pricing page.
How does the RTX 4090 perform for AI and deep learning?
The RTX 4090 delivers strong performance for AI training and inference: 16,384 CUDA cores, 24 GB GDDR6X, and 4th-generation Tensor Cores with native FP8 support. It excels at fine-tuning mid-size LLMs, running diffusion models, and rapid experimentation where iteration speed matters more than maximum VRAM. For context on how it compares to a data center GPU, see the RTX 4090 vs H100 SXM comparison.
Can I rent multiple RTX 4090s in a single instance?
Yes — Runpod supports multi-GPU pod configurations. Note that the RTX 4090 does not support NVLink, so GPUs in a multi-card setup do not share a unified memory pool; each card operates with its own 24 GB. Check real-time availability on the Runpod pricing page for current multi-GPU configurations.
How is data security handled on rented RTX 4090 instances?
Runpod implements isolated environments, data wiping between users, and encryption for data at rest and in transit. For compliance requirements (GDPR, HIPAA, SOC 2), see Runpod's security and compliance documentation and contact the team about Secure Cloud deployment options.
10,100,100,100
Requests since launch & 400k developers worldwide
