Rent the RTX PRO 6000 if…
you need more than 32 GB on one card — 70B-class models at FP8, large rendering scenes, simulation — or ECC and the professional driver stack.
Deploy an RTX PRO 6000 · $749/moQuestions? Open a ticket from your console — an engineer replies within one business day.
The same Blackwell generation, two very different memory sizes: 96 GB of GDDR7 with ECC on the RTX PRO 6000, 32 GB on the RTX 5090 — for 2.7× the monthly price.
The short answer
Both are built on Blackwell's largest workstation-class chip, so per-card speed is in the same league. The difference is memory and the professional feature set: three times the memory, ECC and NVIDIA's professional RTX driver on the RTX PRO 6000. If your model, scene or simulation fits in 32 GB, the RTX 5090 is by far the better value. If it does not — 70B-class LLMs at FP8, large scenes, big CFD or digital-twin jobs — the RTX PRO 6000 is the cheapest single card in the catalogue that holds them.
you need more than 32 GB on one card — 70B-class models at FP8, large rendering scenes, simulation — or ECC and the professional driver stack.
Deploy an RTX PRO 6000 · $749/moyour models and scenes fit in 32 GB and you want the best speed per dollar for generation, inference or rendering.
Deploy an RTX 5090 · $279/moSpecifications
NVIDIA's published figures for each part, tensor throughput without sparsity. The last column is how the RTX PRO 6000 compares, as a multiple of the RTX 5090.
| Specification | NVIDIA RTX PRO 6000 Blackwell | NVIDIA RTX 5090 | RTX PRO 6000 vs RTX 5090 |
|---|---|---|---|
| Architecture | Blackwell · 2025 | Blackwell · 2025 | — |
| GPU memory | 96 GB GDDR7 ECC | 32 GB GDDR7 | 3× |
| Memory bandwidth | 1.6 TB/s | 1.79 TB/s | 0.89× |
| FP16 / BF16 tensor (dense) | ~500 TFLOPS | 210 TFLOPS (419 with FP16 accumulate) | 2.4× |
| FP8 tensor (dense) | ~1,000 TFLOPS | 419 TFLOPS (838 with FP16 accumulate) | 2.4× |
| FP4 tensor (dense) | ~2,000 TFLOPS | 1,676 TFLOPS | 1.2× |
| Maximum power | Up to 600 W | 575 W | 1× |
| Form factor | PCIe Gen5 x16 | PCIe Gen5 x16 | — |
| GPU-to-GPU link | PCIe only | PCIe only | — |
| Multi-Instance GPU (MIG) | Up to 4 instances | No | — |
| ECC memory | Yes | On-die only (GDDR7) | — |
| Video engines | 4 NVENC · 4 NVDEC (AV1) | 3 NVENC · 2 NVDEC (AV1) | — |
| CUDA compute capability | 12.0 | 12.0 | — |
| Dedicated host here | 16 vCPU · 128 GB RAM · 2 TB NVMe | 8 vCPU · 64 GB RAM · 1 TB NVMe | — |
| Price per month here | $749 | $279 | 2.7× |
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 PRO 6000 Blackwell | NVIDIA RTX 5090 | Typical use |
|---|---|---|---|
| 16-bit (BF16 / FP16) | up to ~43B parameters | up to ~14B parameters | Full-precision inference; the base weights of a LoRA fine-tune |
| 8-bit (FP8 / INT8) | up to ~86B parameters | up to ~29B parameters | FP8 serving with vLLM or TensorRT-LLM on GPUs that support it; INT8 weights elsewhere |
| 4-bit (GPTQ, AWQ, Q4) | up to ~160B parameters | up to ~52B 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 PRO 6000 Blackwell | NVIDIA RTX 5090 | Difference per month |
|---|---|---|---|
| Monthly | $749 | $279 | $470 |
| 3 months · −5% | $712 | $265 | $447 |
| 6 months · −10% | $674 | $251 | $423 |
| 12 months · −15% | $637 | $237 | $400 |
| Dedicated host | 16 vCPU · 128 GB RAM · 2 TB NVMe · 10 Gbps | 8 vCPU · 64 GB RAM · 1 TB NVMe · 10 Gbps | included |
Per card they are close: both use Blackwell's largest workstation chip, with slightly more cores enabled on the RTX PRO 6000. The real differences are 96 GB against 32 GB, ECC and the professional driver.
Yes, on one card: at FP8 the weights take about 70 GB, leaving roughly 20 GB of the 96 for the KV cache and the runtime. At 4-bit it leaves over 50 GB for long contexts and concurrency.
For scenes that fit in 32 GB, the RTX 5090 renders about as fast for less than half the price. If the scene would go out of core on 32 GB, the RTX PRO 6000's 96 GB keeps it resident.
6 of 24 RTX PRO 6000 units were free at the last stock update; 11 of 30 RTX 5090 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 — $749 and $279 a month, dedicated, with no identity check and no card.
Dedicated, single-tenant and live in under 10 minutes: the RTX PRO 6000 at $749 or the RTX 5090 at $279 a month, paid in BTC, ETH or USDT with no KYC.