NVIDIA RTX 2000 Ada Generation, AI & Machine Learning Benchmarks & Specs

16GB · AI Score 3.1/100 · first-party measured on 12 AI workloads

3.1 AI Score Includes estimates

Every number on this page is first-party: NVIDIA RTX 2000 Ada Generation was run on our pinned 12-workload AI suite on 2026-07-11, with under 0.5% run-to-run variance. On Llama 3.1 8B (Q4_K_M) NVIDIA RTX 2000 Ada Generation delivers about 43.08 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 16GB. For image generation, SDXL runs at 1.79 it/s, while FLUX.1-dev won't fit at BF16 (needs ~26GB). 6 of the 12 workloads won't fit on 16GB at the tested precision, Qwen3 32B, Llama 3.3 70B, FLUX.1-dev, FLUX.1 Kontext and others. We publish those as hard gates rather than quietly dropping to a smaller quant. NVIDIA RTX 2000 Ada Generation isn't a retail purchase for most people. It's rented by the hour. You can run this exact card on RunPod.

AI & Machine Learning benchmark results

Text Generation tok/s 8

Qwen3 4B70.88

Llama 3.1 8B43.08

Qwen2.5-Coder 14B23.35

Workload	Result	Telemetry	Data
Qwen3 4B	70.88 tok/s	2.8 GB peak35 W50°C2.03 tok/WQ4_K_M	✓ Measured
Llama 3.1 8B	43.08 tok/s	4.7 GB peak44 W54°C0.97 tok/WQ4_K_M	✓ Measured
Qwen2.5-Coder 14B	23.35 tok/s	8.5 GB peak46 W58°C0.51 tok/WQ4_K_M	✓ Measured
Gemma 4 26B A4B	✕ Won't fit needs ~20 GB	VRAM-gated at this precision	Est.
Qwen3 30B A3B	✕ Won't fit needs ~20 GB	VRAM-gated at this precision	Est.
Gemma 4 31B	✕ Won't fit needs ~22 GB	VRAM-gated at this precision	Est.
Qwen3 32B	✕ Won't fit needs ~23 GB	VRAM-gated at this precision	✓ Measured
Llama 3.3 70B	✕ Won't fit needs ~46 GB	VRAM-gated at this precision	✓ Measured

Image Editing images/min 2

Workload	Result	Telemetry	Data
FLUX.1 Kontext dev	✕ Won't fit needs ~26 GB	VRAM-gated at this precision	✓ Measured
Qwen-Image-Edit	✕ Won't fit needs ~42 GB	VRAM-gated at this precision	✓ Measured

Image Generation images/min 2

Workload	Result	Telemetry	Data
Stable Diffusion XL	3.58 images/min	14.5 GB peak68 W60°C16.8 s/img	✓ Measured
FLUX.1 dev	✕ Won't fit needs ~26 GB	VRAM-gated at this precision	✓ Measured

Video Generation frames/s 2

Workload	Result	Telemetry	Data
LTX-Video (distilled)	1.39 frames/s	9.1 GB peak60 W61°C70 s/clip	✓ Measured CPU offload
Wan 2.2 5B (720p)	✕ Won't fit needs ~18 GB	VRAM-gated at this precision	✓ Measured

How we measured this. Every result comes from our own pinned, reproducible AI suite, 12 workloads: the Qwen3-4B to Llama-70B LLM ladder (llama.cpp, Q4_K_M), SDXL / Z-Image / FLUX-dev generation, FLUX-Kontext / Qwen-Edit editing, and LTX / Wan video, run first-party on rented hardware with under 0.5% run-to-run variance. Peak VRAM, power draw, temperature and tokens-per-watt are captured per workload. “Won’t fit” rows are real data: where a model exceeds the card’s VRAM at the tested precision we record a hard gate rather than silently dropping to a smaller quant. Measured 2026-07-11 · harness 2.0.0-standalone.

NVIDIA RTX 2000 Ada Generation specifications

Architecture	Ada Lovelace
CUDA cores	2,816
VRAM	16GB GDDR6
Memory bus	128-bit
Memory bandwidth	224 GB/s
Boost clock	2,130 MHz
TDP	70 W
Process	TSMC 4N
Interface	PCIe 4.0 x8
Release date	2024-02-12
Launch MSRP	$625

Verdict, capable, but 16GB sets the ceiling

NVIDIA RTX 2000 Ada Generation scores 3.1/100, #58 of 102. It ran 5 of 12; 6 exceeded its 16GB. Every figure here is our own measurement.

Relative performance: where the NVIDIA RTX 2000 Ada Generation lands

100% = this card, AI & Machine Learning headline metric (AI Score). #16 of 20 workstation cards in this vertical.

GPU	%	AI Score
NVIDIA RTX A4500	165%	5.1
AMD Radeon Pro W7900	145%	4.5
NVIDIA RTX A4000	132%	4.1
NVIDIA Quadro RTX 5000	113%	3.5
NVIDIA RTX 2000 Ada Generation	100%	3.1
AMD Radeon Pro W6800	97%	3
AMD Radeon Pro W7800	97%	3
Intel Arc Pro A60	58%	1.8
NVIDIA RTX A2000	42%	1.3

One tier up: NVIDIA Quadro RTX 5000 (+13%) · compare head-to-head
One tier down: AMD Radeon Pro W6800 (-3%)

← All AI & Machine Learning GPU rankings

The silicon

Transistors	18,900 million
Die size	158.7 mm²
Process node	4 nm
Fabricated by	TSMC
Transistor density	119.1 million per mm²

Denser than 92% of the 746 cards we have silicon data for. Density is the clearest measure of what a process node bought: a card that gained it without growing the die got its speed from the fab rather than the architecture.

Silicon figures from Wikipedia (CC BY-SA 4.0). Benchmarks on this page are our own. Compare every chip.

What this card can build

Whole-job timings, composed from our measured per-model results on this card.

Workflow	Time	Energy	Basis
Full codebase review	42.8 min	32.76 Wh	measured

Can't run: Product photo shoot (needs FLUX.1 Kontext dev), Photo restoration batch (needs FLUX.1 Kontext dev), Restore and enlarge photos (needs FLUX.1 Kontext dev), Character sheet, 12 poses (needs FLUX.1 dev), Short social clips (needs Qwen3 32B), 60-second AI short film (needs Qwen3 32B), 24-frame storyboard (needs Qwen3 32B), 6-panel comic page (needs Qwen3 32B), Long-form article batch (needs Llama 3.3 70B).

Rent or buy?

This card is $625 to buy. The cheapest listed rate on RunPod is $0.240/hour, but that is the floor: we budget $0.288/hour, a 20% premium, because idle time, storage and unavailable cheap instances all land on the same bill. At that rate buying wins after 2,170 GPU-hours. Below it you are paying for idle silicon.

How you would use it	GPU-hours a year	Rental cost a year	Time to break even
2 hours a day, hobby	730	$210	3.0 years
8 hours a day, working on it	2,920	$841	8.9 months
24/7, always-on agent	8,760	$2,523	3.0 months

At steady usage this card pays for itself inside a normal ownership window. Rental figures include a 20% premium over the cheapest listed rate. Ignores electricity, resale and the fact that a rented card can be a newer one tomorrow.

Rental price

$0.240/hr+0.0% since 2026-08-14low $0.240 · high $0.240

Cheapest of the RunPod and Vast on-demand rates we see, sampled daily. Spot and interruptible pricing runs lower.