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RTX PRO 6000 vs RTX 5090 for AI work

By the NEOXIS team · 4 min read

Both cards use NVIDIA's Blackwell architecture and GDDR7 memory. The RTX 5090 is a GeForce card for a desk; the RTX PRO 6000 is a professional card with three times the memory, and its Server Edition is built for data centres.

If Choose Why
Your models fit in 32 GB and you work on one workstation RTX 5090, launched at $1,999 The lowest cost per GPU and very fast memory
Your model and its context need more than 32 GB on one card, or the GPU runs around the clock in a server RTX PRO 6000, rent our 8-GPU server from $1.70 per GPU-hour 96 GB with ECC per card, MIG, made for data centres

The differences that matter for AI

RTX 5090 RTX PRO 6000 Server Edition
GPU memory 32 GB GDDR7 96 GB GDDR7 with ECC
Memory bandwidth 1,792 GB/s 1,597 GB/s
CUDA cores 21,760 24,064
Maximum power 575 W up to 600 W, configurable
MIG instances not listed by NVIDIA up to 4 per GPU
Cooling fans on the card passive, cooled by the server

Memory: the RTX PRO 6000 holds three times as much

For large language models, memory decides what fits. A 70B model in FP8 needs about 70 GB for its weights alone. On a 96 GB RTX PRO 6000 it fits on one card, with room left for the context cache. On 32 GB cards the same model has to be split across at least three GPUs, and every split adds traffic between them over PCIe.

With FP4, which both cards run natively, a 70B model needs about 35 GB. That still does not fit on one RTX 5090, but it leaves a large cache on one RTX PRO 6000. For models up to about 30B parameters, 32 GB is usually enough, and the RTX 5090 handles them well.

Speed for one conversation: the RTX 5090 is not slower

When a model answers one user, the GPU reads the weights for every new token, so memory bandwidth sets the pace. The RTX 5090 has slightly more bandwidth than the RTX PRO 6000 Server Edition (1,792 against 1,597 GB/s). For a model that fits in 32 GB, a single conversation runs at least as fast on the RTX 5090.

The RTX PRO 6000 pulls ahead when the model or the batch does not fit in 32 GB. Then the 5090 needs several cards and the links between them, while the PRO 6000 keeps everything on one GPU. Our comparison of the RTX PRO 6000 and the H100 explains the same trade-off against a data-centre GPU.

In a server: the RTX PRO 6000 is built for it

The RTX PRO 6000 Server Edition has error-correcting (ECC) memory, which catches memory errors during long runs. It can be split into up to four isolated MIG instances, so several users or models can share one card safely. It is passively cooled, which lets eight of them sit in one chassis with the server's own fans.

The RTX 5090 is a desktop card with its own fans. NVIDIA's GeForce software licence also states that GeForce software "is not licensed for datacenter deployment" (section 2.8), so check the terms before you plan a server around it.

Buying a card or renting a server

An RTX 5090 is a sensible purchase for one developer who works with models up to about 30B parameters on a desk. If you need 96 GB per GPU, several GPUs in one machine or a server that runs all month, renting can be simpler than buying. Our dedicated GPU server has eight RTX PRO 6000 Server Edition cards, 768 GB of GPU memory in total, full root access and a 99.5% SLA, hosted in the EU. The editions of the RTX PRO 6000 are compared in Max-Q vs Workstation vs Server Edition.

Questions

Can I run a 70B model on one RTX 5090? Not in FP8 or FP4 with a useful context: the weights alone need about 70 GB or 35 GB. On one RTX PRO 6000 it fits in both formats.

Does the RTX 5090 support FP4? Yes. Both cards are Blackwell GPUs with fifth-generation Tensor Cores and native FP4.

Is the RTX PRO 6000 Workstation Edition different from the Server Edition? The memory is the same 96 GB. The Workstation Edition has its own fans and 1,792 GB/s; the Server Edition is passive, made for servers, with 1,597 GB/s.

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