NVIDIA RTX 4000 (Ada Generation), AI & Machine Learning Benchmarks & Specs

20GB · AI Score 4.4/100 · first-party measured on 12 AI workloads

4.4 AI Score ✓ Measured

Every number on this page is first-party: NVIDIA RTX 4000 (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 4000 (Ada Generation) delivers about 66.59 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 20GB. For image generation, SDXL runs at 3.28 it/s, while FLUX.1-dev won't fit at BF16 (needs ~26GB). 5 of the 12 workloads won't fit on 20GB 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 4000 (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 5

Qwen3 4B110.18

Llama 3.1 8B66.59

Qwen2.5-Coder 14B36.34

Workload	Result	Telemetry	Data
Qwen3 4B	110.18 tok/s	2.9 GB peak68 W46°C1.63 tok/WQ4_K_M	✓ Measured
Llama 3.1 8B	66.59 tok/s	4.8 GB peak82 W55°C0.81 tok/WQ4_K_M	✓ Measured
Qwen2.5-Coder 14B	36.34 tok/s	8.6 GB peak83 W63°C0.44 tok/WQ4_K_M	✓ Measured
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 Generation images/min 2

Workload	Result	Telemetry	Data
Stable Diffusion XL	6.56 images/min	14.6 GB peak123 W66°C9.2 s/img	✓ Measured
FLUX.1 dev	✕ Won't fit needs ~26 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

Video Generation frames/s 2

Workload	Result	Telemetry	Data
LTX-Video (distilled)	2.06 frames/s	9.2 GB peak96 W70°C47.1 s/clip	✓ Measured
Wan 2.2 5B (720p)	0.18 frames/s	16.6 GB peak116 W79°C269.7 s/clip	✓ 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 4000 (Ada Generation) specifications

Architecture	Ada Lovelace
CUDA cores	6,144
VRAM	20GB GDDR6 ECC
Memory bus	160-bit
Memory bandwidth	360 GB/s
Boost clock	2,175 MHz
TDP	130 W
Process	4nm
Interface	PCIe 4.0 x16
Release date	2023-08-09
Launch MSRP	$1,250

Verdict, capable, but 20GB sets the ceiling

NVIDIA RTX 4000 (Ada Generation) scores 4.4/100, #50 of 102. It ran 6 of 12; 5 exceeded its 20GB. Every figure here is our own measurement.

Relative performance: where the NVIDIA RTX 4000 (Ada Generation) lands

100% = this card, AI & Machine Learning headline metric (AI Score). #15 of 61 desktop cards in this vertical.

GPU	%	AI Score
GeForce RTX 4080 Super	123%	5.4
GeForce RTX 5080	111%	4.9
NVIDIA GeForce RTX 4080	107%	4.7
GeForce RTX 5070 Ti	107%	4.7
NVIDIA RTX 4000 (Ada Generation)	100%	4.4
NVIDIA GeForce RTX 4070 Ti Super	98%	4.3
GeForce RTX 5060 Ti	84%	3.7
AMD Radeon RX 6950 XT	80%	3.5
NVIDIA GeForce RTX 3080 Ti	80%	3.5

One tier up: GeForce RTX 5070 Ti (+7%) · compare head-to-head
One tier down: NVIDIA GeForce RTX 4070 Ti Super (-2%) · compare head-to-head
Best value alternative ($749): GeForce RTX 5070 Ti (+7%) · compare head-to-head

← All AI & Machine Learning GPU rankings

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	27.5 min	38.11 Wh	measured

Can't run: 60-second AI short film (needs Qwen3 32B), 60-second AI short film, narrated (needs Qwen3 32B), 10 short social clips (needs Qwen3 32B), 40-product photo shoot (needs FLUX.1 Kontext dev), 6-panel comic page (needs Qwen3 32B), 20 long-form articles (needs Llama 3.3 70B), Character sheet, 12 poses (needs FLUX.1 dev), 100-photo restoration batch (needs FLUX.1 Kontext dev), 24-frame storyboard (needs Qwen3 32B), 100-photo restore and enlarge (needs FLUX.1 Kontext dev).

Rent or buy?

This card is $1,250 to buy. The cheapest listed rate on RunPod is $0.200/hour, but that is the floor: we budget $0.240/hour, a 20% premium, because idle time, storage and unavailable cheap instances all land on the same bill. At that rate buying wins after 5,208 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	$175	7.1 years
8 hours a day, working on it	2,920	$701	1.8 years
24/7, always-on agent	8,760	$2,102	7.1 months

At hobby usage this card is very unlikely to pay for itself before it is superseded. Rent it. 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

$0.200/hr+0.0% since 2026-08-14low $0.200 · high $0.200

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