Rent the RTX 5090 if…
you generate video or high-resolution images, run 30B-class models at 4-bit, or want the fastest single consumer GPU for inference.
Deploy an RTX 5090 · $279/moQuestions? Open a ticket from your console — an engineer replies within one business day.
Blackwell against Ada: 32 GB of GDDR7 at 1.79 TB/s and FP4 on the RTX 5090, against 24 GB of GDDR6X at 1 TB/s on the RTX 4090 — for 1.5× the monthly price.
The short answer
The RTX 5090 is the faster card by a wide margin: a third more memory, 78% more bandwidth and a third more CUDA cores, which shows most in image and video generation and in LLM token generation. The RTX 4090 remains the best value in the catalogue: if your models and batches fit in 24 GB — SDXL, FLUX, 7B–13B LLMs — it does the job for $189 a month.
you generate video or high-resolution images, run 30B-class models at 4-bit, or want the fastest single consumer GPU for inference.
Deploy an RTX 5090 · $279/moyour models and batches fit in 24 GB and price matters most — SDXL, FLUX, LoRA training, 7B–13B LLM serving.
Deploy an RTX 4090 · $189/moSpecifications
NVIDIA's published figures for each part, tensor throughput without sparsity. The last column is how the RTX 5090 compares, as a multiple of the RTX 4090.
| Specification | NVIDIA RTX 5090 | NVIDIA RTX 4090 | RTX 5090 vs RTX 4090 |
|---|---|---|---|
| Architecture | Blackwell · 2025 | Ada Lovelace · 2022 | — |
| GPU memory | 32 GB GDDR7 | 24 GB GDDR6X | 1.3× |
| Memory bandwidth | 1.79 TB/s | 1 TB/s | 1.8× |
| FP16 / BF16 tensor (dense) | 210 TFLOPS (419 with FP16 accumulate) | 165 TFLOPS (330 with FP16 accumulate) | 1.3× |
| FP8 tensor (dense) | 419 TFLOPS (838 with FP16 accumulate) | 330 TFLOPS (661 with FP16 accumulate) | 1.3× |
| FP4 tensor (dense) | 1,676 TFLOPS | Not supported | Only the RTX 5090 |
| Maximum power | 575 W | 450 W | 1.3× |
| Form factor | PCIe Gen5 x16 | PCIe Gen4 x16 | — |
| GPU-to-GPU link | PCIe only | PCIe only | — |
| Multi-Instance GPU (MIG) | No | No | — |
| ECC memory | On-die only (GDDR7) | No | — |
| Video engines | 3 NVENC · 2 NVDEC (AV1) | 2 NVENC · 1 NVDEC (AV1) | — |
| CUDA compute capability | 12.0 | 8.9 | — |
| Dedicated host here | 8 vCPU · 64 GB RAM · 1 TB NVMe | 8 vCPU · 64 GB RAM · 1 TB NVMe | — |
| Price per month here | $279 | $189 | 1.5× |
Sources: NVIDIA datasheets and product pages for each GPU. Tensor figures are dense (NVIDIA often prints them doubled “with sparsity”). Prices are this site's flat monthly rates for a dedicated server with the host listed, in USD excluding VAT.
Value
Price per month divided into the two resources most jobs run out of first — memory and memory bandwidth — and what the same GPU costs kept on for a month at the market-median hourly rate.
Model sizes
The largest model whose weights fit on a single GPU, keeping 10% of the memory for the runtime, activations and a small KV cache. Long contexts and large batches need more — size them with the GPU memory calculator.
| Weights precision | NVIDIA RTX 5090 | NVIDIA RTX 4090 | Typical use |
|---|---|---|---|
| 16-bit (BF16 / FP16) | up to ~14B parameters | up to ~11B parameters | Full-precision inference; the base weights of a LoRA fine-tune |
| 8-bit (FP8 / INT8) | up to ~29B parameters | up to ~22B parameters | FP8 serving with vLLM or TensorRT-LLM on GPUs that support it; INT8 weights elsewhere |
| 4-bit (GPTQ, AWQ, Q4) | up to ~52B parameters | up to ~39B parameters | Quantised inference on a budget; QLoRA fine-tuning |
Parameters in billions, weights only: 2 bytes per parameter at 16-bit, 1 at FP8 or INT8, about 0.55 at 4-bit (GPTQ, AWQ, GGUF Q4). Mixture-of-experts models need memory for all their experts, not just the active ones.
Prices
Per dedicated GPU server, in USD excluding VAT — CPU cores, RAM, NVMe, 20 TB of transfer and 24/7 support included.
| Term | NVIDIA RTX 5090 | NVIDIA RTX 4090 | Difference per month |
|---|---|---|---|
| Monthly | $279 | $189 | $90 |
| 3 months · −5% | $265 | $180 | $85 |
| 6 months · −10% | $251 | $170 | $81 |
| 12 months · −15% | $237 | $161 | $76 |
| Dedicated host | 8 vCPU · 64 GB RAM · 1 TB NVMe · 10 Gbps | 8 vCPU · 64 GB RAM · 1 TB NVMe · 10 Gbps | included |
It depends on what limits the job. Bandwidth-bound work such as LLM token generation can gain up to the 1.78× bandwidth difference; compute-bound work such as diffusion gains roughly in line with the extra cores. The 5090 also fits models the 4090 cannot.
Not on one card: a 70B model at 4-bit needs about 39 GB for its weights alone. For a single GPU, the RTX PRO 6000 (96 GB, $749 a month) runs 70B at FP8; two RTX 5090s can split a 70B model at 4-bit.
Yes. Both are workstation-class GPUs here: Windows Server 2022 or 2025 with the RTX driver and RDP is available at checkout for $29 a month.
11 of 30 RTX 5090 units were free at the last stock update; 19 of 36 RTX 4090 units were free at the last stock update (18 September 2026). Both are provisioned automatically, in under 10 minutes after payment.
Yes. Both are rented with an email address and paid from a prepaid balance in Bitcoin, Ether or USDT — $279 and $189 a month, dedicated, with no identity check and no card.
Dedicated, single-tenant and live in under 10 minutes: the RTX 5090 at $279 or the RTX 4090 at $189 a month, paid in BTC, ETH or USDT with no KYC.