NVIDIA RTX 4500 Ada Generation, AI & Machine Learning Benchmarks & Specs

24GB · AI Score 5.9/100 · anchored estimate vs 51 measured cards

5.9 AI Score Includes estimates

We have not run NVIDIA RTX 4500 Ada Generation on our bench. These figures are anchored estimates, interpolated per workload against the 51 GPUs we did measure. On Llama 3.1 8B (Q4_K_M) NVIDIA RTX 4500 Ada Generation should deliver about 77.5 tokens/sec. Stepping up to Qwen3 32B it should hold roughly 18.2 tok/s. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 24GB. For image generation, SDXL should run near 4.16 it/s, while FLUX.1-dev won't fit at BF16 (needs ~26GB). 4 of the 12 workloads won't fit on 24GB at the tested precision, Llama 3.3 70B, FLUX.1-dev, FLUX.1 Kontext, Qwen-Image-Edit. We publish those as hard gates rather than quietly dropping to a smaller quant.

AI & Machine Learning benchmark results

Text Generation tok/s 11

Granite 4.1 3B150.79

Qwen3 30B A3B146.27

Qwen3-4B132.17

Gemma 4 26B A4B104.28

Llama-3.1-8B78.87

Qwen3 8B76.96

Gemma 4 12B49.61

Qwen3 14B43.7

Qwen2.5-Coder-14B43.38

Qwen3 32B18.2

Workload	Result	Telemetry	Data
Granite 4.1 3B	150.79 tok/s	98 W58°CQ4_K_M	✓ Measured
Qwen3-4B	132.17 tok/s	104 W58°CQ4_K_M	✓ Measured
Llama-3.1-8B	78.87 tok/s	117 W56°CQ4_K_M	✓ Measured
Qwen3 8B	76.96 tok/s	116 W58°CQ4_K_M	✓ Measured
Gemma 4 12B	49.61 tok/s	117 W61°CQ4_K_M	✓ Measured
Qwen2.5-Coder-14B	43.38 tok/s	130 W62°CQ4_K_M	✓ Measured
Qwen3 14B	43.7 tok/s	125 W61°CQ4_K_M	✓ Measured
Gemma 4 26B A4B	104.28 tok/s	74 W58°CQ4_K_M	✓ Measured
Qwen3 30B A3B	146.27 tok/s	66 W55°CQ4_K_M	✓ Measured
Qwen3 32B	18.2 tok/s	estimated	Est.
Llama 3.3 70B	✕ Won't fit	VRAM-gated at this precision	Est.

Image Generation images/min 3

Workload	Result	Telemetry	Data
Stable Diffusion XL	8.32 images/min	estimated	Est.
FLUX.1 dev	✕ Won't fit	VRAM-gated at this precision	Est.
Z-Image Turbo	1.73 images/min	estimated	Est.

Image Editing images/min 2

Workload	Result	Telemetry	Data
FLUX.1 Kontext dev	✕ Won't fit	VRAM-gated at this precision	Est.
Qwen-Image-Edit	✕ Won't fit	VRAM-gated at this precision	Est.

Video Generation frames/s 2

Workload	Result	Telemetry	Data
LTX-Video (distilled)	1.94 frames/s	estimated	Est.
Wan 2.2 5B (720p)	0.25 frames/s	estimated	Est.

How this estimate is derived. This card hasn’t been through our bench yet, so its numbers are anchored estimates, interpolated from the 51 first-party measured cards (LLM: bandwidth Theil-Sen ladder · diffusion/video: tensor-throughput ladder within architecture family · gates: realistic Q4/BF16 VRAM floors). The VRAM “won’t fit” gates are exact, since they’re pure capacity limits. Estimates are replaced with measured data as more silicon goes through the bench. Full methodology →

NVIDIA RTX 4500 Ada Generation specifications

Architecture	Ada Lovelace
CUDA cores	7,680
VRAM	24GB GDDR6 ECC
Memory bus	192-bit
Memory bandwidth	432 GB/s
Boost clock	2,580 MHz
TDP	210 W
Process	4nm
Interface	PCIe 4.0 x16
Release date	2023-08-09
Launch MSRP	$2,250

Verdict, capable, but 24GB sets the ceiling

NVIDIA RTX 4500 Ada Generation scores 5.9/100, #38 of 102. It ran 8 of 12; 4 exceeded its 24GB. Figures are anchored estimates, not measurements, we flag that on every row.

Relative performance: where the NVIDIA RTX 4500 Ada Generation lands

100% = this card, AI & Machine Learning headline metric (AI Score). #11 of 20 workstation cards in this vertical.

GPU	%	AI Score
NVIDIA Quadro RTX 8000	139%	8.2
NVIDIA RTX PRO 4000 Blackwell	129%	7.6
NVIDIA Quadro RTX 6000 (Turing)	125%	7.4
NVIDIA RTX A5000	125%	7.4
NVIDIA RTX 4500 Ada Generation	100%	5.9
NVIDIA RTX A4500	86%	5.1
AMD Radeon Pro W7900	76%	4.5
NVIDIA RTX A4000	69%	4.1
NVIDIA Quadro RTX 5000	59%	3.5

One tier up: NVIDIA RTX A5000 (+25%) · compare head-to-head
One tier down: NVIDIA RTX A4500 (-14%) · compare head-to-head

← All AI & Machine Learning GPU rankings

The silicon

Transistors	35,800 million
Die size	294.5 mm²
Process node	4 nm
Fabricated by	TSMC
Transistor density	121.6 million per mm²

Denser than 94% 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
60-second AI short film	15.1 min	n/a	estimate, 0 of 3 stages measured
24-frame storyboard	15.2 min	n/a	estimate, 0 of 2 stages measured
Full codebase review	23.1 min	49.95 Wh	measured
Short social clips	38.9 min	n/a	estimate, 0 of 3 stages 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), 6-panel comic page (needs FLUX.1 dev), Long-form article batch (needs Llama 3.3 70B).

Rent or buy?

This card is $2,250 to buy. The cheapest listed rate on Vast.ai is $0.389/hour, but that is the floor: we budget $0.467/hour, a 20% premium, because idle time, storage and unavailable cheap instances all land on the same bill. At that rate buying wins after 4,820 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	$341	6.6 years
8 hours a day, working on it	2,920	$1,363	1.7 years
24/7, always-on agent	8,760	$4,089	6.6 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

$0.389/hr+5.7% since 2026-09-30low $0.362 · high $0.470

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