RTX 5090 vs RTX PRO 6000: when is 32 GB enough?

The RTX 5090 vs RTX PRO 6000 decision is simpler than most GPU comparisons, because both cards are NVIDIA Blackwell. The GeForce RTX 5090 is a consumer card with 32 GB of GDDR7. The RTX PRO 6000 Blackwell Server Edition is a professional card with 96 GB of GDDR7 with ECC, and it supports MIG. We rent the RTX 5090 at $449 a month and the RTX PRO 6000 at $1,390. The real question is whether your work fits in 32 GB.

RTX 5090 vs RTX PRO 6000: the short answer

If your models and data fit in 32 GB with headroom, the RTX 5090 does the job for about a third of the price. That covers most image generation, 7B to 14B language models and 4-bit models around 30B parameters. If you need more memory, or ECC and MIG for production serving, choose the RTX PRO 6000. There is little middle ground: once a job outgrows 32 GB, you are offloading to system RAM, quantizing harder or splitting work, and each of those has a cost. Our RTX 5090 plan also has 64 GB of system RAM, which limits how much you can offload.

When 32 GB is enough

Roughly, weights take 2 bytes per parameter in BF16, 1 in FP8 and 0.5 in 4-bit formats, plus 10–30% for KV cache, activations and overhead.

For the first five rows, the RTX PRO 6000's extra memory buys batch size and context length, not the ability to run. A 14B model in FP8 leaves about 18 GB on the 5090 for KV cache and overhead, enough for a handful of concurrent conversations. To put numbers on it with a smaller model: Llama 3.1 8B in BF16 takes about 16 GB, and its KV cache about 0.13 MB per token in 16-bit. The 16 GB left on the 5090 holds roughly 15 conversations of 8k tokens at once, before overhead; the 80 GB left on the RTX PRO 6000 holds roughly 75. LoRA fine-tuning of 7B to 8B models and QLoRA on 14B models also fit in 32 GB.

When you need 96 GB

• 70B-class models. Even in 4-bit, a 70B model needs about 35 GB for its weights. Our single-GPU plans are separate virtual machines, so you cannot split a model across two RTX 5090s the way you would inside one server.
• 32B-class models in FP8 or BF16. 32 GB of FP8 weights fill the 5090 before any KV cache.
• Video generation. Video models attend across all frames, so memory grows with resolution and clip length. With 96 GB you can keep the model in BF16 and generate longer clips without offloading.
• Long contexts and many users. After 35 GB of 4-bit 70B weights, the RTX PRO 6000 still has about 61 GB for KV cache.
• Large render scenes. GPU renderers need the scene in memory; past 32 GB they fall back to slower out-of-core modes or fail.

Our guide to VRAM for LLMs walks through the KV cache math behind these numbers.

Same architecture, similar bandwidth

Both cards are Blackwell GPUs with the same tensor-core formats, FP8 and FP4 included, and the same CUDA support, so code that runs on one runs on the other. NVIDIA lists 1.79 TB/s of memory bandwidth for the RTX 5090 and about 1.6 TB/s for the RTX PRO 6000, so the 5090 actually has about 12% more. On compute, NVIDIA quotes 3,352 AI TOPS of FP4 with sparsity for the RTX 5090 and 4 PFLOPS of FP4 with sparsity for the RTX PRO 6000, about 20% more on paper.

Power is close too: 575 W for the 5090, up to 600 W for the RTX PRO 6000. Neither has NVLink; both use PCIe Gen5. The specifications that really separate them are memory capacity and the professional features.

ECC, MIG and the professional card

NVIDIA specifies ECC for the RTX PRO 6000's memory. ECC detects and corrects single-bit memory errors, which matters for long training runs, production inference and any job where a silent error costs more than a crash. The RTX 5090 is a GeForce card, designed and marketed for consumers.

The RTX PRO 6000 also supports MIG, which NVIDIA lists at up to four isolated instances per card, each with its own memory and compute: for example, four slices of roughly 24 GB serving four small models with hard isolation. We rent whole GPUs and never partition them ourselves; if you plan to use MIG, mention it in the order notes for our engineers.

The RTX PRO 6000 is also the card we offer as an 8-GPU server: 768 GB of GPU memory for $10,490 a month. The RTX 5090 is single-GPU only.

Cost per month and value

The RTX 5090 costs $449 a month, about $0.62 per GPU-hour. The RTX PRO 6000 costs $1,390, about $1.90. Per gigabyte of GPU memory they are almost identical: about $14.03 against $14.48 a month. You do not pay a premium for the professional card's memory; you buy it in one 96 GB piece.

That makes the choice about shape. Three RTX 5090s cost $1,347 a month, $43 less than one RTX PRO 6000, and give you three independent 32 GB GPUs: a good fit for parallel image generation or three separate small-model endpoints. One RTX PRO 6000 gives you a single 96 GB GPU, the only one of the two that holds a 70B model. Each single-GPU plan also includes 20 TB of outbound bandwidth a month under fair use, so three RTX 5090 plans come with three times the included transfer of one RTX PRO 6000. Our generative media page covers both setups, and the GPU cost calculator compares totals.

How to decide

1. Measure peak memory on your heaviest job. Under about 26 GB, choose the RTX 5090 and keep headroom.
2. If you need ECC or MIG for production, choose the RTX PRO 6000 whatever the size.
3. Between 26 and 32 GB, test on the 5090 first: the minimum term is one month and you can cancel any month with no fee.
4. Above 32 GB, or planning 70B-class models, choose the RTX PRO 6000. Check sizes with the VRAM calculator.