RTX 5880 Ada vs RTX 6000 Ada: specs, vGPU support and which 48 GB card fits the workload

RTX 5880 Ada vs RTX 6000 Ada: the short answer

The RTX 5880 Ada Generation is a 48 GB professional card with the same memory as the RTX 6000 Ada: 48 GB of GDDR6 with ECC on a 384-bit interface at 960 GB/s. It has 22.5 per cent fewer CUDA, Tensor and RT Cores and draws 285 W instead of 300 W. Both are dual-slot, actively cooled PCIe 4.0 cards of the same size, with the same display outputs, the same vGPU editions and no MIG. For work that is bound by memory size and bandwidth, such as generating tokens in LLM inference, the two behave alike; for work bound by the cores, such as rendering, FP32 simulation and long prompts, the RTX 6000 Ada has about 31 per cent more peak throughput on paper.

Both cards belong to the Ada Lovelace generation that the RTX PRO Blackwell cards succeeded. How the whole range maps across the two generations is in our RTX Ada vs RTX PRO Blackwell line-up comparison, and this article stays with the two 48 GB Ada cards.

RTX 5880 Ada specs next to the RTX 6000 Ada

NVIDIA RTX 5880 Ada datasheet (documents 3082478, December 2023, and 3156635, February 2024) and RTX 6000 Ada datasheet (2647623, February 2023), product pages read on 10 October 2026; MIG from NVIDIA’s MIG user guide (11 September 2026). Peak rates at GPU Boost Clock; the Tensor figure is effective FP8 with sparsity.

The peak figures fall slightly more than the core counts. The RTX 5880 Ada has 77.5 per cent of the RTX 6000 Ada’s cores and 76 per cent of its FP32, RT Core and Tensor throughput, so the clock behind NVIDIA’s peak rates is also a little lower. NVIDIA’s CUDA GPUs page lists the RTX 6000 Ada at compute capability 8.9 and does not list the RTX 5880 Ada, while the RTX 5880 Ada product page states that its fourth-generation Tensor Cores “support acceleration of the FP8 precision data type”. Neither card has FP4 Tensor Cores, which arrived with Blackwell.

What fewer cores change and what they do not

A GPU workload is limited either by how fast data comes out of memory or by how fast the cores compute on it. The memory side is identical on these two cards, so every job limited by bandwidth or by capacity runs the same on both. The 15 W lower board power is the only difference there, and a model, scene or dataset that fits one card fits the other.

Work that keeps the cores busy scales with the peak figures. Path-traced rendering uses the RT and CUDA cores, simulation in FP32 the CUDA cores, and training, fine-tuning and the prompt phase of inference the Tensor Cores. On these, the RTX 6000 Ada offers 31 per cent more peak throughput. Peak rates are an upper limit, and the measured gap depends on the application, its clocks under load and the cooling of the machine. We found no paired test of the two cards from a named source with a stated method, so this article quotes no measured difference.

LLM inference on 48 GB at 960 GB/s

Generating a token with a dense model reads all of its weights from memory once. Per conversation, the upper limit is the bandwidth divided by the size of the weights. A 32-billion-parameter model in FP8 takes at least 32 GB, one byte per parameter, and with the same 960 GB/s both cards share the same ceiling for a single conversation; measured rates stay below it. A serving engine such as vLLM batches many conversations into one pass over the weights, so total throughput grows with the number of users until the KV cache fills the memory or the cores become the limit. Both cards reach that point with the same 48 GB.

The cores matter in the prefill phase, when the model reads the prompt. RAG with long retrieved passages, agents with long tool outputs and document summarisation all spend more of their time there, and on those the RTX 6000 Ada’s extra Tensor Cores shorten the time to first token. For a chat assistant with short prompts the two cards are close.

Memory sets the model size. A 32B model in FP8 fits one card with cache for about 8 conversations of 8K with ECC off, or 5 with ECC on, by the sizing rule in our 48 GB comparison, while a 70B model in FP8 needs about 70 GB for its weights alone and so needs two cards, split by tensor or pipeline parallelism. Neither card has NVLink, so a split model exchanges data over PCIe 4.0. A 70B model in a 4-bit checkpoint loads on one card but, by the same rule, leaves no room for one full 8K conversation. Neither card has FP4 Tensor Cores, so check that your serving engine supports the checkpoint’s 4-bit format on Ada. Where one card should hold 70B in FP8, or where MIG should divide a card between teams, the RTX PRO 6000 Blackwell comparison with the RTX 6000 Ada covers the 96 GB card. The other 48 GB options, the RTX PRO 5000 Blackwell and the L40S, are set side by side in our 48 GB GPU comparison.

Rendering, simulation and video

For GPU rendering in path tracers, the RT Core and CUDA core counts decide the speed, and the RTX 6000 Ada has 29 per cent more of both. For a render farm that bills by frames per hour, or a design team that waits on interactive previews, that is the main reason to choose the RTX 6000 Ada. A scene that fits 48 GB on one fits on the other, so the RTX 5880 Ada does not limit scene size.

FP32 simulation scales with the 69.3 against 91.1 TFLOPS. Neither datasheet gives an FP64 rate, so a solver that needs double precision should be checked against a data-centre GPU such as the H200 NVL.

Video is the same on both: three encode and three decode engines, with AV1 encode and decode, in both datasheets. A video pipeline that is limited by the number of encoder engines gains nothing from the RTX 6000 Ada.

vGPU, NVIDIA AI Enterprise and MIG on the RTX 5880 Ada

Both datasheets list NVIDIA vPC/vApps and NVIDIA RTX Virtual Workstation as the supported vGPU software, with the profiles in NVIDIA’s Virtual GPU Licensing Guide. On both, the four DisplayPort outputs are on by default, and NVIDIA’s product pages say to turn them off when using vGPU software. NVIDIA’s vGPU release notes for vSphere (29 September 2026) list the RTX 6000 Ada among the cards that “must be used in NVIDIA vGPU software deployments in display-off mode”, set with the displaymodeselector tool. NVIDIA’s RTX 5880 Ada datasheet said its virtualisation support would come in a vGPU release “anticipated in Q1, 2024”; NVIDIA’s list of GPUs supported by vGPU, updated on 2 October 2026, gives release 17.0 as its first and 15.2 for the RTX 6000 Ada, both with full support and no end date set.

NVIDIA’s vGPU support matrix for VMware, updated on 29 September 2026, names both cards for VCF 9.1, VCF 9.0 and ESXi 8.0, the last from 8.0 Update 3 P06. NVIDIA AI Enterprise 8.2 lists both among its Ada Lovelace discrete GPUs, beside the RTX 5000 Ada, L40S and L4. Passthrough, vGPU modes, vMotion and the licence per concurrent user are explained in our guide to GPUs in VMware vSphere, passthrough or vGPU.

Neither card supports MIG. NVIDIA’s MIG user guide lists no Ada Lovelace GPU, so on both cards the only way to share one GPU between virtual machines is time-sliced vGPU. MIG starts in this class with the RTX PRO 5000 and 6000 Blackwell.

Where the RTX 5880 Ada sits in NVIDIA’s range

NVIDIA’s datasheet for the RTX 5880 Ada is dated December 2023, and its product page presents a card that gives “data scientists, engineers, and creative professionals the large memory needed to work with large datasets”. In NVIDIA’s current vGPU and AI Enterprise documents the two cards appear side by side, with the same vGPU editions. We found no NVIDIA document that names a Blackwell successor for the RTX 5880 Ada.

Choosing cards for a fleet of 4 to 8

Our reading of the specifications in the first table and of NVIDIA’s vGPU and MIG documents, as of October 2026; no measured comparison is implied.

Take eight design workstations with one card each. The RTX 5880 Ada gives every seat the same 48 GB and the same memory bandwidth as the RTX 6000 Ada, and the eight cards draw at most 2,280 W of board power against 2,400 W. If the users render or simulate for much of the day, the RTX 6000 Ada’s cores shorten every job, and that time adds up across eight seats. A mixed fleet can follow the roles, with the RTX 6000 Ada for the render and simulation seats and the RTX 5880 Ada for modelling and review.

We supply the RTX 5880 Ada, the RTX 6000 Ada and the RTX PRO Blackwell cards beside them on one EU contract and invoice. Tell us the workloads and how many workstations you are equipping, and we answer with the card for each seat.

For a server with four cards, four RTX 5880 Ada give 192 GB in total at 1,140 W of board power, four RTX 6000 Ada the same at 1,200 W. That holds two copies of a 70B model in FP8, each split over two cards with cache for about 9 conversations of 8K with ECC off (5 with ECC on) by the rule in our 48 GB comparison, or four copies of a 32B model in FP8, one per card. Both are actively cooled workstation cards, so a rack server needs a model whose maker lists the card in that slot, and the difference to the passive L40S is set out in our RTX 6000 Ada vs L40S comparison.

We check the rack, power and airflow before we quote. Send us the server model or rack position through the form below, with the cards you have in mind.

What we supply

We supply the RTX 5880 Ada and the RTX 6000 Ada as professional NVIDIA GPUs, with manufacturer warranty and on one EU contract and invoice, together with the RTX PRO 6000 and RTX PRO 5000 Blackwell where 96 GB, MIG or FP4 are needed. We build AI servers to order with these cards, assembled and burn-in tested, and configuration and quote follow within one business day. NVIDIA vGPU and AI Enterprise licences for both cards go on the same invoice as the hardware.

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