NVIDIA RTX A4000, AI & Machine Learning Benchmarks & Specs

16GB · AI Score 4.1/100 · first-party measured on 12 AI workloads

4.1 AI Score ✓ Measured

Every number on this page is first-party: NVIDIA RTX A4000 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 A4000 delivers about 75.52 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 16GB. For image generation, SDXL runs at 2.62 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 A4000 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 4B116.23

Llama 3.1 8B75.52

Qwen2.5-Coder 14B41.13

Workload	Result	Telemetry	Data
Qwen3 4B	116.23 tok/s	2.9 GB peak73 W49°C1.6 tok/WQ4_K_M	✓ Measured
Llama 3.1 8B	75.52 tok/s	4.8 GB peak84 W59°C0.9 tok/WQ4_K_M	✓ Measured
Qwen2.5-Coder 14B	41.13 tok/s	8.6 GB peak74 W63°C0.55 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	5.24 images/min	15.7 GB peak139 W66°C11.5 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 1

Workload	Result	Telemetry	Data
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 A4000 specifications

Architecture	Ampere
CUDA cores	6,144
VRAM	16GB GDDR6
Memory bus	256-bit
Memory bandwidth	448 GB/s
Boost clock	1,560 MHz
TDP	140 W
Process	8nm
Interface	PCIe 4.0 x16
Release date	2021-04-12
Launch MSRP	$999

Verdict, capable, but 16GB sets the ceiling

NVIDIA RTX A4000 scores 4.1/100, #52 of 102. It ran 4 of 12; 6 exceeded its 16GB. Every figure here is our own measurement.

Relative performance: where the NVIDIA RTX A4000 lands

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

GPU	%	AI Score
AMD Radeon Pro W7800	151%	6.2
NVIDIA RTX 4500 Ada Generation	144%	5.9
AMD Radeon Pro W6800	134%	5.5
NVIDIA RTX A4500	124%	5.1
NVIDIA RTX A4000	100%	4.1
NVIDIA Quadro RTX 5000	85%	3.5
NVIDIA RTX 2000 Ada Generation	76%	3.1
Intel Arc Pro A60	56%	2.3
NVIDIA RTX A2000	32%	1.3

One tier up: NVIDIA RTX A4500 (+24%) · compare head-to-head
One tier down: NVIDIA Quadro RTX 5000 (-15%)

← 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	24.3 min	30.11 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 $999 to buy. The cheapest listed rate on RunPod is $0.170/hour, but that is the floor: we budget $0.204/hour, a 20% premium, because idle time, storage and unavailable cheap instances all land on the same bill. At that rate buying wins after 4,897 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	$149	6.7 years
8 hours a day, working on it	2,920	$596	1.7 years
24/7, always-on agent	8,760	$1,787	6.7 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

$0.170/hr+0.0% since 2026-08-14low $0.170 · high $0.170

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