NVIDIA Quadro RTX 5000, AI & Machine Learning Benchmarks & Specs

16GB · AI Score 3.5/100 · anchored estimate vs 51 measured cards

3.5 AI Score Includes estimates

We have not run NVIDIA Quadro RTX 5000 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 Quadro RTX 5000 should deliver about 81 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 16GB. For image generation, SDXL should run near 1.57 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.

AI & Machine Learning benchmark results

Text Generation tok/s 12

Granite 4.1 3B130.34

Qwen3-4B118.26

Llama-3.1-8B73.61

Qwen3 8B72.21

Gemma 4 12B46.37

Qwen3 14B41.8

Qwen2.5-Coder-14B41.11

Workload	Result	Telemetry	Data
Granite 4.1 3B	130.34 tok/s	145 W49°CQ4_K_M	✓ Measured
Qwen3-4B	118.26 tok/s	157 W50°CQ4_K_M	✓ Measured
Llama-3.1-8B	73.61 tok/s	170 W47°CQ4_K_M	✓ Measured
Qwen3 8B	72.21 tok/s	173 W50°CQ4_K_M	✓ Measured
Gemma 4 12B	46.37 tok/s	180 W54°CQ4_K_M	✓ Measured
Qwen2.5-Coder-14B	41.11 tok/s	191 W55°CQ4_K_M	✓ Measured
Qwen3 14B	41.8 tok/s	186 W55°CQ4_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	VRAM-gated at this precision	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	3.14 images/min	estimated	Est.
FLUX.1 dev	✕ Won't fit	VRAM-gated at this precision	Est.
Z-Image Turbo	1.5 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)	1.88 frames/s	estimated	Est.
Wan 2.2 5B (720p)	✕ Won't fit	VRAM-gated at this precision	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 Quadro RTX 5000 specifications

Architecture	Turing (TU104)
CUDA cores	3,072
VRAM	16GB GDDR6
Memory bus	256-bit
Memory bandwidth	448 GB/s
Boost clock	1,770 MHz
TDP	230 W
Process	12nm
Interface	PCIe 3.0 x16
Release date	2018-08-14
Launch MSRP	$2,299

Verdict, capable, but 16GB sets the ceiling

NVIDIA Quadro RTX 5000 scores 3.5/100, #52 of 102. It ran 6 of 12; 6 exceeded its 16GB. Figures are anchored estimates, not measurements, we flag that on every row.

Relative performance: where the NVIDIA Quadro RTX 5000 lands

100% = this card, AI & Machine Learning headline metric (AI Score). #15 of 20 workstation cards in this vertical.

GPU	%	AI Score
NVIDIA RTX 4500 Ada Generation	169%	5.9
NVIDIA RTX A4500	146%	5.1
AMD Radeon Pro W7900	129%	4.5
NVIDIA RTX A4000	117%	4.1
NVIDIA Quadro RTX 5000	100%	3.5
NVIDIA RTX 2000 Ada Generation	89%	3.1
AMD Radeon Pro W6800	86%	3
AMD Radeon Pro W7800	86%	3
Intel Arc Pro A60	51%	1.8

One tier up: NVIDIA RTX A4000 (+17%) · compare head-to-head
One tier down: NVIDIA RTX 2000 Ada Generation (-11%) · compare head-to-head
Best value alternative ($625): NVIDIA RTX 2000 Ada Generation (-11%) · compare head-to-head

← All AI & Machine Learning GPU rankings

The silicon

Transistors	13,600 million
Die size	545 mm²
Process node	12 nm
Fabricated by	TSMC
Transistor density	25 million per mm²

Denser than 69% 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	24.4 min	77.39 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).

Rental price

$0.096/hr+0.0% since 2026-09-30low $0.096 · high $0.096

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