Buy the RTX 5090 for one person's daily work under about 29 GB, and rent the RTX PRO 6000 Blackwell for models between 30 and 88 GB, for BF16 training, or for 96 GB over fewer hours than a card's price buys. Both cards are NVIDIA's GB202 chip, compute capability 12.0, with FP4 and FP8 Tensor Cores. The RTX PRO 6000 enables 188 of the chip's 192 streaming multiprocessors to the 5090's 170, carries 96 GB of GDDR7 with ECC against 32 GB, and runs its FP16, BF16 and FP8 Tensor Core math at full rate with FP32 accumulate, where the 5090 drops to half. Memory bandwidth is the same 1,792 GB/s on the Workstation Edition and lower, 1,597 GB/s, on the Server Edition, so for one user on a model that fits in 32 GB, the 5090 reads memory as fast or faster.
RTX PRO 6000 Blackwell and RTX 5090 specs#
The two cards share a chip and differ in how much of it they enable, how much memory surrounds it, and how they accumulate.
| Spec | RTX PRO 6000 Blackwell | GeForce RTX 5090 |
|---|---|---|
| Chip (compute capability) | GB202 (12.0) | GB202 (12.0) |
| SMs and CUDA cores | 188 and 24,064 | 170 and 21,760 |
| Tensor Cores | 752, fifth generation | 680, fifth generation |
| GPU memory | 96 GB GDDR7 with ECC | 32 GB GDDR7, ECC built into the memory dies |
| Memory bandwidth | 1,792 GB/s (Workstation and Max-Q), 1,597 GB/s (Server Edition) | 1,792 GB/s |
| L2 cache | 128 MB (Workstation and Max-Q) | 96 MB |
| FP4 Tensor Core, dense | 2,015.2 TFLOPS | 1,676 TFLOPS |
| FP8 Tensor Core, dense, FP16 accumulate | 1,007.6 TFLOPS | 838 TFLOPS |
| FP8 Tensor Core, dense, FP32 accumulate | 1,007.6 TFLOPS | 419 TFLOPS |
| BF16 Tensor Core, dense, FP32 accumulate | 503.8 TFLOPS | 209.5 TFLOPS |
| FP32 | 126 TFLOPS (Server Edition: 120) | 104.8 TFLOPS |
| Power | 600 W (Server Edition: up to 600 W, configurable) | 575 W |
| Multi-Instance GPU | Up to 4 instances of 24 GB | No |
| Video engines | 4 NVENC, 4 NVDEC | 3 NVENC, 2 NVDEC |
| NVLink | Not supported | No |
| How you get one | Rent it by the hour on QuantaCloud, 1, 2 or 4 per VM on 2026-09-27 | Buy it: $1,999 at launch in 2025 |
All Tensor Core figures are dense, without sparsity. The RTX PRO 6000 figures are for the Workstation Edition, from NVIDIA's RTX PRO Blackwell architecture whitepaper, and the 5090 figures come from the RTX Blackwell whitepaper. NVIDIA's Server Edition datasheet gives 120 TFLOPS of FP32, 1,597 GB/s and "4 PFLOPS" of FP4 without saying whether that figure assumes sparsity. The Workstation Edition datasheet marks its 4,000 AI TOPS as FP4 with sparsity, twice the dense figure in the table.
QuantaCloud's catalog lists the card only as RTX PRO 6000 Blackwell, which leaves the edition, and so the bandwidth row, open until you look. The RTX PRO 6000 page explains how the three editions differ.
Same chip, different accumulate rates#
Beyond memory, the difference is how each card accumulates. NVIDIA lists the RTX PRO 6000 at the same Tensor Core rate whether its FP16 or FP8 math accumulates in FP16 or FP32. The 5090, a GeForce card, halves those rates with FP32 accumulate: 419 dense FP8 TFLOPS instead of 838, and 209.5 in BF16, which NVIDIA lists only with FP32 accumulate. With FP16 accumulate, and in FP4, the Workstation Edition leads by 1.2 times, which is what 11 percent more SMs at a 9 percent higher boost clock add up to (our calculation: 188 / 170 x 2,617 / 2,407 = 1.20). With FP32 accumulate, and in BF16, it leads by 2.4 times (503.8 / 209.5).
A 5090's price in RTX PRO 6000 hours#
At the 5090's launch price, the break-even is about 836 hours. The 5090 went on sale in the US on January 30, 2025 at $1,999, NVIDIA's launch price, and on 2026-09-05 Tom's Hardware reported it "regularly listed for above $5,000". The RTX PRO 6000 rents from $2.39/GPU-hr on QuantaCloud. At $2.39 an hour for a 1x on 2026-09-27, $1,999 buys 836 hours of it and $5,000 buys 2,092 (our calculation: 1,999 / 2.39 and 5,000 / 2.39).
Prices checked 5 Oct 2026, 18:47 UTC
This is how long those hours last, next to the price of buying an RTX PRO 6000 outright:
| Price of the card | RTX PRO 6000 hours at $2.39 | At 20 hours a week | At 40 hours a week |
|---|---|---|---|
| $1,999, the 5090 at launch | 836 | 42 weeks | 21 weeks |
| $5,000, the 5090 street level Tom's Hardware reported | 2,092 | 105 weeks | 52 weeks |
| $16,000, the RTX PRO 6000 Workstation Edition on NVIDIA's US store in August 2026 | 6,695 | 335 weeks | 167 weeks |
Our calculation: the card's price divided by $2.39, then by the hours per week. Tom's Hardware reported on 2026-08-12 that NVIDIA's US store listed the RTX PRO 6000 Workstation Edition at $16,000, up from $8,565 for a single card when it went on sale in April 2025. Per GB of memory, those dated prices put the two cards close together: $167 for the RTX PRO 6000 at $16,000 and $156 for a 5090 at $5,000, against $62 at the 5090's launch price (our calculation: 16,000 / 96, 5,000 / 32 and 1,999 / 32).
Neither number is the full cost. A 5090 needs a PC with a power supply for a 575 W card, and 836 hours at that limit can draw up to 481 kWh (0.575 kW x 836 hours). The RTX PRO 6000's hourly price covers a VM that on 2026-09-27 had 16 vCPUs, 144 GB of RAM and 725 GB of disk, in us-east-1 and us-midwest-4, and stopping it terminates the VM and deletes that disk, so copy results off first. The unused seconds of the current hour are refunded, as the pricing page explains.
Launch an RTX PRO 6000Models that need 96 GB on one GPU#
At vLLM's default of 92 percent, a 5090 gives a model 29.3 GiB and the RTX PRO 6000 87.9 GiB (our calculation from the 32,607 and 97,887 MiB that nvidia-smi reports).
| Workload | Memory it needs | RTX 5090 | RTX PRO 6000 | Basis |
|---|---|---|---|---|
| Qwen3-32B, NVIDIA's NVFP4 build | 20.7 GB checkpoint, 8 GiB of KV per 32k conversation | Yes, about 41,000 tokens of KV | Yes, 8.6 conversations at once | Published file size, our calculation |
| Gemma 4 31B | 23.3 GB for Google's 4-bit build, 62.5 GB in BF16 | The 4-bit build, with about 7.6 GiB left for KV cache | BF16, with 29.7 GiB left | Published file sizes, our calculation |
| Qwen3-32B in BF16 | 65.5 GB checkpoint | No | Yes, 3.4 conversations at 32k | Published file size, our calculation |
| gpt-oss-120b | 65.2 GB of weights, 4.5 GiB of KV per full 131,072-token sequence | No | Yes, 6.0 full-length sequences | Published, our calculation |
| Llama-3.3-70B, NVIDIA's NVFP4 build | 42.7 GB checkpoint | No | Yes, about 158,000 tokens of BF16 KV | Published file size, our calculation |
| Llama-3.3-70B, per-channel FP8 | 72.7 GB checkpoint | No | Tight: about 66,000 tokens of BF16 KV | Published file size, our calculation |
| FLUX.2 [dev] | 35.46 GB fp8 transformer, 18.03 GB fp8 text encoder, 21.04 GB NVFP4 transformer | The NVFP4 transformer, with the text encoder offloaded | Both fp8 files at once, 53.5 GB | Published file sizes, our sum |
| Wan 2.2 A14B video at 720p | 59.8 GB peak on one GPU, with offloading | No | Yes | Published (Wan) |
| QLoRA, gpt-oss-120b | 65 GB (Unsloth) | No | Yes | Published |
| 16-bit LoRA, 30B to 34B model | 64 to 80 GB (Axolotl) | No | Yes | Published |
| QLoRA, 70B model | 41 GB (Unsloth), 40 to 48 GB (Axolotl) | No | Yes | Published |
The 70B rows are the point of this pair. NVIDIA's NVFP4 build of Llama-3.3-70B runs with FP4 math on either chip, but only the RTX PRO 6000 can hold it, with room left for more than one full 131,072-token context, which takes 42.9 GB at BF16. FLUX.2 and Wan are softer limits, since ComfyUI can offload weights to system RAM on either card at a cost in speed. The NVFP4 explainer covers the format, the KV cache explainer the context arithmetic, and the VRAM guide models not listed here.
Where the 5090 is the better buy#
Buy the 5090 for daily work under about 29 GB, where the bigger card's memory would go unused.
FP4 is not a reason to rent the bigger card, because both have it. vLLM runs NVFP4 checkpoints natively on Blackwell, and ComfyUI computes in NVFP4 on compute capability 10 and newer when PyTorch is a CUDA 13 build. NVIDIA's NVFP4 build of Qwen3-32B, 20.7 GB, fits a 5090 with room for one 32k-token conversation, and Black Forest Labs' NVFP4 file of FLUX.1 [dev] is 9.19 GB.
One user generating tokens is the second case, because bandwidth is the one row where the 5090 can come out ahead. It reads memory at the Workstation Edition's 1,792 GB/s, so one stream from a model that fits in 32 GB is capped at about the same speed on either card, and against the Server Edition's 1,597 GB/s it has 12 percent more (our calculation: 1,792 / 1,597 = 1.12).
The software is the third, because it is the same on both. Blackwell needs CUDA 12.8 or newer and PyTorch builds with sm_120 kernels, and since the 5090 and the RTX PRO 6000 share compute capability 12.0, a stack that runs on a rented RTX PRO 6000 has the kernels a 5090 needs. RTX A6000 vs RTX 5090 walks through the error older builds throw. Past the break-even, each hour on your own card costs electricity, and your files stay on your disk.
Where renting the RTX PRO 6000 pays#
Rent the RTX PRO 6000 when the job needs 30 to 88 GB on one GPU, or runs BF16 math for hours.
Models that have to sit on one GPU are the main case. A 70B model at FP4 or FP8, gpt-oss-120b, Qwen3-32B or Gemma 4 31B in BF16, and Wan 2.2 at 720p all fit on one RTX PRO 6000 and on no single 5090. The gpt-oss GPU requirements guide covers that model in detail.
Training is the second. BF16 math, which NVIDIA lists only with FP32 accumulate, runs at 503.8 dense TFLOPS on the Workstation Edition against 209.5 on the 5090, and QLoRA on a 70B model or 16-bit LoRA on a 32B model needs memory a 5090 does not have.
Short projects are the third. If you need 96 GB for a few hundred hours this year, 836 hours of RTX PRO 6000 time cost what a 5090 did at launch. For more, the 4x VM on 2026-09-27 put 384 GB in one machine at $9.53 an hour.
Three 5090s match the 96 GB, but not on one GPU. They cost $5,997 at the launch price and $15,000 at the $5,000 street level, draw up to 1,725 W at their board limits, and talk over PCIe with no NVLink, where vLLM recommends pipeline parallelism. NVIDIA's driver license also says GeForce software "is not licensed for datacenter deployment" (section 2.8).
FAQ#
Is the RTX PRO 6000 Blackwell faster than the RTX 5090?
For math with FP32 accumulate, including BF16, yes: 2.4 times on NVIDIA's dense figures for the Workstation Edition. With FP16 accumulate and in FP4, by about 1.2 times. For one user generating tokens from a model that fits in 32 GB, no: bandwidth sets that pace, and it is the same 1,792 GB/s on the Workstation Edition and 12 percent lower on the Server Edition.
Can I rent an RTX 5090 on QuantaCloud?
No. The 5090 is a GeForce card, and the GPU QuantaCloud rents on the 5090's GB202 chip is the RTX PRO 6000. Every card it rents is in the GPU catalog with its live price. Past 96 GB on one GPU, the H200 NVL has 141 GB, and RTX PRO 6000 vs A100 and RTX 6000 Ada vs RTX 5090 cover the 80 GB A100 and the 48 GB RTX 6000 Ada.
Can I split an RTX PRO 6000 into smaller GPUs?
The card can: MIG divides it into up to four isolated 24 GB instances, which the 5090 cannot do. On 2026-09-27 every QuantaCloud offer was a full 96 GB GPU, so treat MIG as a feature of the card, not of the catalog.
The rule I use for this pair: the 5090 is the better buy for one person's daily work under about 29 GB, since the chip and, on the Workstation Edition, the bandwidth are the same. For models between 30 and 88 GB, BF16 training, or 96 GB for fewer hours than a card's price buys, rent the RTX PRO 6000, and run nvidia-smi -q on the first VM to see which edition you have. Launch an RTX PRO 6000