NVIDIA RTX A4500, AI & Machine Learning Benchmarks & Specs

20GB · AI Score 5.1/100 · first-party measured on 12 AI workloads

5.1 AI Score Includes estimates

Every number on this page is first-party: NVIDIA RTX A4500 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 A4500 delivers about 100.2 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 20GB. For image generation, SDXL runs at 3.25 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 A4500 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 6

Qwen3 4B148.87

Llama 3.1 8B100.2

Qwen2.5-Coder 14B54.29

Workload	Result	Telemetry	Data
Qwen3 4B	148.87 tok/s	2.9 GB peak117 W42°C1.27 tok/WQ4_K_M	✓ Measured
Llama 3.1 8B	100.2 tok/s	4.6 GB peak136 W48°C0.74 tok/WQ4_K_M	✓ Measured
Qwen2.5-Coder 14B	54.29 tok/s	8.6 GB peak137 W55°C0.4 tok/WQ4_K_M	✓ Measured
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	6.5 images/min	15.7 GB peak199 W59°C9.2 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)	2.39 frames/s	9.4 GB peak169 W69°C40.5 s/clip	✓ Measured CPU offload
Wan 2.2 5B (720p)	0.21 frames/s	16.6 GB peak192 W75°C234.6 s/clip	✓ Measured CPU offload

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 A4500 specifications

Architecture	Ampere (workstation variant)
CUDA cores	7,168
VRAM	20GB GDDR6
Memory bus	320-bit
Memory bandwidth	640 GB/s
Boost clock	1,575 MHz
TDP	200 W
Process	8nm
Interface	PCIe 4.0 x16
Release date	2021-11-23
Launch MSRP	$1,199

Verdict, capable, but 20GB sets the ceiling

NVIDIA RTX A4500 scores 5.1/100, #40 of 102. It ran 6 of 12; 5 exceeded its 20GB. Every figure here is our own measurement.

Relative performance: where the NVIDIA RTX A4500 lands

100% = this card, AI & Machine Learning headline metric (AI Score). #12 of 20 workstation cards in this vertical.

GPU	%	AI Score
NVIDIA RTX PRO 4000 Blackwell	149%	7.6
NVIDIA Quadro RTX 6000 (Turing)	145%	7.4
NVIDIA RTX A5000	145%	7.4
NVIDIA RTX 4500 Ada Generation	116%	5.9
NVIDIA RTX A4500	100%	5.1
AMD Radeon Pro W7900	88%	4.5
NVIDIA RTX A4000	80%	4.1
NVIDIA Quadro RTX 5000	69%	3.5
NVIDIA RTX 2000 Ada Generation	61%	3.1

One tier up: NVIDIA RTX 4500 Ada Generation (+16%) · compare head-to-head
One tier down: AMD Radeon Pro W7900 (-12%)

← All AI & Machine Learning GPU rankings

The silicon

Transistors	28,300 million
Die size	628.4 mm²
Process node	8 nm
Fabricated by	Samsung
Transistor density	45 million per mm²

Denser than 81% 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	18.4 min	42.18 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 $1,199 to buy. The cheapest listed rate on RunPod is $0.190/hour, but that is the floor: we budget $0.228/hour, a 20% premium, because idle time, storage and unavailable cheap instances all land on the same bill. At that rate buying wins after 5,259 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	$166	7.2 years
8 hours a day, working on it	2,920	$666	1.8 years
24/7, always-on agent	8,760	$1,997	7.2 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

$0.190/hr+0.0% since 2026-08-14low $0.125 · high $0.190

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