NVIDIA RTX 6000 Ada
VRAM
48 GB
Memory
GDDR6 ECC
Bandwidth
960 GB/s
FP16
364 TF
TDP
300 W
Memory and compute
Architecture and power
Notes
FP16 dense tensor per NVIDIA datasheet. Previous-gen 48GB workstation card, still common in on-prem inference builds.
Models that may fit on NVIDIA RTX 6000 Ada
Estimates based on INT8 quantization. Actual fit depends on framework and batch size.
More GPUs by NVIDIA
The manufacturer's other cards in the catalog, largest memory first, with bandwidth and power where published.
Frequently asked questions about NVIDIA RTX 6000 Ada
Answered from the entry's own figures: memory, which models fit, bandwidth, power and the card's class.
NVIDIA RTX 6000 Ada has 48 GB of GDDR6 ECC at 960 GB/s. At INT8 a model's weights take about one byte per parameter, so the card holds roughly a 38B-parameter model with room for context; larger models need several cards or a lower precision.
From the catalog's estimates at INT8, Gemma 4 31B IT, Qwen 2.5 32B Instruct, DeepSeek R1 Distill Qwen 32B and Aya Vision 32B fit on a single NVIDIA RTX 6000 Ada with their default context. The list on this page shows the estimated memory each takes; the capacity planner sizes any model against this card for your own context length, batch size and request rate.
960 GB/s. Bandwidth bounds how fast a model generates tokens, because every token reads the whole set of weights and the KV cache from memory; at this rate a 38B model at INT8 could be read on the order of 25 times a second on one card, before batching and framework efficiency.
NVIDIA RTX 6000 Ada has a TDP of 300 W, in the PCIe form factor. Budget the TDP per card plus host overhead when sizing a server's power and cooling.
NVIDIA RTX 6000 Ada is a workstation card, a PCIe board for a single machine, built on the Ada Lovelace architecture and released in 2022. It has no multi-GPU interconnect, so a model must fit on one card or be split over the slower PCIe bus.
Specifications from the manufacturer's published material; a figure that is not published is shown as such, and a derived one is explained in the notes.
Added Jul 30, 2026 · Last updated Jul 30, 2026
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