# Rent a dedicated RTX 5090 cloud server for $449 per month > Rent a whole RTX 5090 (32 GB GDDR7) in a dedicated VM for $449/month, about $0.62 per GPU-hour at 24/7. For image generation, small LLMs and dev work. Source: https://overgpu.com/gpus/nvidia-rtx-5090 Updated: 2026-10-23 Publisher: OverGPU (https://overgpu.com/) **In short:** OverGPU rents the NVIDIA GeForce RTX 5090 by the month: a dedicated single GeForce RTX 5090 (32 GB GDDR7) for $449 per month, about $0.62 per GPU-hour at full use. It is in stock and delivered in under 10 minutes after payment. Payment is from a USD balance topped up in Bitcoin, Ethereum, Monero, USDT (TRC-20) or Solana, with no setup fee, and the plan can be cancelled before any monthly renewal. ## RTX 5090 configurations and monthly prices at OverGPU | Configuration | Type | GPU memory | CPU | RAM | Storage | Network | Price per month (USD) | Per GPU-hour | Availability | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | [NVIDIA GeForce RTX 5090](https://overgpu.com/gpus/nvidia-rtx-5090) | Single GPU | 32 GB GDDR7 | 16 vCPU | 64 GB | 1 TB NVMe | 10 Gbps | $449 | $0.62 | In stock, delivered in under 10 minutes after payment | Per GPU-hour = monthly price ÷ number of GPUs ÷ 730 hours, shown only to compare with hourly offers: every plan is billed as one flat monthly price. ## NVIDIA GeForce RTX 5090 specifications (vendor-published) | Specification | Value | Note | | --- | --- | --- | | Architecture | Blackwell | NVIDIA | | GPU memory | 32 GB GDDR7 | | | Memory bandwidth | 1.79 TB/s | | | FP4 | 3,352 AI TOPS | Vendor figure, with sparsity | | FP8 | Supported | No dense figure published | | BF16 / FP16 | Supported | No dense figure published | | GPU interconnect | PCIe Gen5 x16 | no NVLink | | Form factor | PCIe card · GeForce | | | Max power | 575 W | | ## What the RTX 5090 is best at - **Image generation:** SDXL with large batches, and 12B-parameter image models such as FLUX.1 in FP8. - **Development and prototyping:** A persistent CUDA box to build and test code before moving it to H100 or H200 servers. - **Small and quantized LLMs:** 8B models in BF16, or 32B models in 4-bit, fit in 32 GB. - **Video drafts and previews:** Smaller or FP8 video models for drafts and previews, at $449 per month. ## When another GPU fits better - [Rent RTX PRO 6000 Blackwell servers from $1,390 per month](https://overgpu.com/gpus/nvidia-rtx-pro-6000): When you need more than 32 GB, for example video models in BF16 or 70B LLMs, with 96 GB per GPU. - [Rent NVIDIA H100 SXM servers from $2,190 per month](https://overgpu.com/gpus/nvidia-h100): For production LLM serving or fine-tuning on a data-center GPU with 80 GB of HBM3. - [Rent a dedicated NVIDIA L40S for $890 per month](https://overgpu.com/gpus/nvidia-l40s): When 48 GB is enough and you want a data-center card for 14B models in BF16, at $890 per month. ## Overview If you want to rent RTX 5090 power without buying and hosting the card yourself, we offer one whole GeForce RTX 5090 with 32 GB of GDDR7 in a dedicated virtual machine for $449 per month. It is our lowest-priced plan and a good fit for image generation, small or quantized language models, prototyping and development, as long as your work fits in 32 GB. Below, we show exactly where that limit bites. ## What the RTX 5090 is, and what it is not The RTX 5090 is NVIDIA's Blackwell GeForce flagship. Per NVIDIA's specifications, it has 32 GB of GDDR7 at 1.79 TB/s, connects over PCIe Gen5 and draws up to 575 W, and its Tensor Cores support low-precision formats down to FP4, which NVIDIA lists at 3,352 AI TOPS with sparsity. For models that fit in memory, that bandwidth is high for the price: more than the L40S (864 GB/s) or the RTX PRO 6000 (1.6 TB/s). It is still a GeForce card designed for desktops, not a data-center GPU. It has no NVLink, its memory stops at 32 GB, and we rent it as a single-GPU plan only. Treat it as a fast, affordable GPU for work that fits, not as a substitute for an H100 in production. If you are upgrading from an older card, our page on [how it compares with the previous GeForce generation](https://overgpu.com/compare/rtx-4090-vs-rtx-5090) covers the differences, and [RTX 5090 vs RTX PRO 6000](https://overgpu.com/compare/rtx-5090-vs-rtx-pro-6000) shows when to step up. ## What fits in 32 GB, and what does not Weights take roughly 2 bytes per parameter in BF16, 1 in FP8 and 0.5 in 4-bit, plus KV cache or activations and 10–30% headroom. - **Image generation:** SDXL, with about 3.5B parameters, takes around 7 GB in FP16 and leaves room for large batches. A 12B-parameter model such as FLUX.1 needs about 24 GB for its BF16 weights alone, so on 32 GB you run it in FP8 (about 12 GB) or let the pipeline unload the text encoder once the prompt is encoded. - **Video generation:** a 14B-parameter video model needs about 28 GB in BF16 before activations, which does not leave enough room. Use FP8 weights, a smaller model variant, or a 96 GB RTX PRO 6000. - **LLM inference:** an 8B model in BF16 (about 16 GB) leaves roughly 10 GB for KV cache after headroom; a 14B model fits in FP8 (about 14 GB) but not in BF16; a 32B model fits in 4-bit (about 16 GB). A 70B model does not fit, even in 4-bit (about 35 GB). For scale, a Llama-style 8B model with grouped-query attention (32 layers, 8 KV heads) needs about 0.13 MB of KV cache per token in BF16, so 10 GB holds roughly 75,000 tokens across all active requests. - **Training:** LoRA on image models and QLoRA on 8B–14B LLMs work. Full fine-tuning with Adam needs roughly 16–20 bytes per parameter, which limits it to models of around 1B parameters. The plan's 64 GB of system RAM also limits how much you can offload to the CPU, so treat 32 GB as a firm ceiling. When you reach it, move video and large image pipelines to the RTX PRO 6000 and LLM serving to the L40S or H100; our [generative media guide](https://overgpu.com/solutions/generative-media) covers sizing for media workloads. ## A development box with the same CUDA stack An RTX 5090 also works well as a persistent development machine. You get root access over SSH, Ubuntu 22.04 or 24.04 with NVIDIA drivers, CUDA, Docker and the NVIDIA Container Toolkit preinstalled, and 1 TB of NVMe where downloaded models and datasets stay between sessions. Code written here runs on our H100 and H200 servers with the same CUDA tooling, as long as any compiled extensions target both architectures, so you can prototype at $449 per month and move to a data-center GPU when the workload grows. One practical point: Blackwell needs recent software. Install PyTorch builds for CUDA 12.8 or later; older builds do not include kernels for this GPU and stop with a "no kernel image is available" error. A quick check after setup: ``` nvidia-smi # GPU model, driver and CUDA version python -c "import torch; print(torch.cuda.get_device_name(0), torch.version.cuda)" ``` Editors that work over SSH connect directly, and web tools such as JupyterLab or ComfyUI are best reached through an SSH tunnel rather than a public port. Our guides to [NVIDIA drivers and CUDA](https://overgpu.com/docs/nvidia-drivers-cuda) and to [remote development](https://overgpu.com/docs/remote-development) go further. ## RTX 5090 price per month and per GPU-hour The RTX 5090 costs $449 per month, about $0.62 per GPU-hour if it runs all 730 hours. That suits an always-on generation queue or a small endpoint serving traffic around the clock. For a development box, it depends on how you count: used 8 hours a day on 22 working days (176 hours), it costs about $2.55 per used hour, in exchange for an environment that is always ready. For a single weekend of work, hourly billing elsewhere usually costs less. To find your break-even point, divide $449 by the hourly rate you would pay. The price includes 20 TB of outbound traffic per month with no egress fees within that allowance, a public IPv4 address and an IPv6 /64, DDoS mitigation, 24/7 support from engineers and the 99.9% availability SLA, with no setup fee. Your environment stays as you left it for as long as you keep the plan. ## Ordering an RTX 5090 [Deploy an RTX 5090](https://overgpu.com/deploy) from one form: choose the configuration, set up the OS image, optional preinstalled software and your login (root password or SSH key), create a free account or log in, and pay the first month from your prepaid balance, which you top up in BTC, ETH, XMR, USDT on TRON or SOL ([how crypto payment works](https://overgpu.com/pay-with-crypto)). If your balance is short, you top up in the same form; the order is held for 3 hours and paid automatically as soon as the top-up is credited. Card payments are coming soon. Most orders need only an account and a funded balance. The server is delivered in under 10 minutes after payment, with the IP and SSH details in your dashboard. You can cancel any month with no fee. Once you have an active server, you can ask for volume pricing on several 5090s for a team or a class in a ticket from your dashboard, giving the quantity and duration. ## Frequently asked questions ### How much does it cost to rent an RTX 5090? $449 per month for one whole RTX 5090, about $0.62 per hour if it runs all month. There is no setup fee, and 20 TB of outbound traffic per month is included. ### Is the RTX 5090 a data-center GPU? No. It is a GeForce card with 32 GB of GDDR7, which we rent for image generation, small models and development. For production LLM work, our data-center GPUs such as the L40S, RTX PRO 6000 and H100 are the better fit. ### Can I run a 70B model on an RTX 5090? No. Even in 4-bit, a 70B model needs about 35 GB for its weights alone, more than the card's 32 GB. Use an RTX PRO 6000 or an H100 instead. ### Which PyTorch version do I need? A build for CUDA 12.8 or later, which includes kernels for Blackwell GPUs. Older builds stop with a "no kernel image is available" error. ### Can I get several RTX 5090s in one server? No, we rent the RTX 5090 as a single-GPU plan only. For multi-GPU work, see the 8-GPU RTX PRO 6000 server or our HGX servers. ### Can I pay for an RTX 5090 in crypto? Yes. You top up a prepaid balance in US dollars with Bitcoin (BTC), Ethereum (ETH), Monero (XMR), USDT on TRON (TRC-20) or Solana (SOL), at an exchange rate locked for a limited time, and the plan is paid from that balance. Card payments are coming soon. ## Related pages - [Single GPU server rental: a whole GPU in a dedicated VM](https://overgpu.com/products/single-gpu-servers): One whole GPU in a dedicated cloud VM, from RTX 5090 to B300. Root access, 20 TB bandwidth, monthly billing, delivered in under 10 minutes. - [GPU cloud for AI startups, with predictable monthly spend](https://overgpu.com/solutions/startups): Dedicated H100, H200 and Blackwell GPUs at a flat monthly price, paid in crypto, cancel any month. See how to size and budget. - [GPU servers for universities and research labs, on fixed budgets](https://overgpu.com/solutions/universities): Fixed monthly prices for grants and semesters, invoices for procurement and term pricing. Plan a semester. - [LLM VRAM calculator: will your model fit on a GPU?](https://overgpu.com/tools/vram-calculator): Pick Llama, Qwen, Mistral or gpt-oss, set the precision and context length, and see weights, KV cache and which GPUs fit. - [How to choose a GPU server for AI](https://overgpu.com/blog/how-to-choose-a-gpu-server): A framework for memory, bandwidth, precision, interconnect, CPU and storage, software and billing, plus a checklist. - [Dedicated vs shared GPU: what changes when you share](https://overgpu.com/blog/dedicated-vs-shared-gpus): How vGPU, MIG, time-slicing and shared hosts affect memory, consistency, security and debugging, and when sharing is fine. - [RTX 5090 vs RTX 4090 for AI: what 8 GB more memory buys you](https://overgpu.com/compare/rtx-4090-vs-rtx-5090): 32 GB vs 24 GB, 1.79 TB/s vs 1,008 GB/s and FP4. Which LLMs and image models fit, and what renting a 5090 costs per month. - [RTX 5090 vs RTX PRO 6000: when is 32 GB enough?](https://overgpu.com/compare/rtx-5090-vs-rtx-pro-6000): A 32 GB card at $449 a month or a 96 GB pro card with ECC and MIG at $1,390. See exactly when 32 GB is enough for your work.