NVIDIA GeForce RTX 2080 Founders Edition, AI & Machine Learning Benchmarks & Specs

8GB · AI Score 2.0/100 · anchored estimate vs 51 measured cards

2 AI Score Includes estimates

We have not run NVIDIA GeForce RTX 2080 Founders Edition 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 GeForce RTX 2080 Founders Edition should deliver about 81 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 8GB. For image generation, SDXL should run near 1.37 it/s, while FLUX.1-dev won't fit at BF16 (needs ~26GB). 9 of the 12 workloads won't fit on 8GB at the tested precision, Qwen2.5-Coder 14B, Qwen3 32B, Llama 3.3 70B, Z-Image Turbo 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 9

Workload	Result	Telemetry	Data
Qwen3 4B	126.4 tok/s	estimated	Est.
Llama 3.1 8B	81 tok/s	estimated	Est.
Qwen2.5-Coder 14B	✕ Won't fit	VRAM-gated at this precision	Est.
Qwen3 14B	✕ Won't fit needs ~11 GB	VRAM-gated at this precision	Est.
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 5

Workload	Result	Telemetry	Data
Sana 1.6B	✕ Won't fit needs ~11 GB	VRAM-gated at this precision	Est.
Stable Diffusion XL	2.74 images/min	estimated	Est.
PixArt-Sigma XL	✕ Won't fit needs ~14 GB	VRAM-gated at this precision	Est.
FLUX.1 dev	✕ Won't fit	VRAM-gated at this precision	Est.
Z-Image Turbo	✕ Won't fit	VRAM-gated at this precision	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)	✕ Won't fit	VRAM-gated at this precision	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 GeForce RTX 2080 Founders Edition specifications

Architecture	Turing (TU104)
CUDA cores	2,944
VRAM	8GB GDDR6
Memory bus	256-bit
Memory bandwidth	448 GB/s
Boost clock	1,710 MHz
TDP	215 W
Process	12nm
Interface	PCIe 3.0 x16
Release date	2018-09-20
Launch MSRP	$699

Verdict, capable, but 8GB sets the ceiling

NVIDIA GeForce RTX 2080 Founders Edition scores 2.0/100, #85 of 102. It ran 3 of 12; 9 exceeded its 8GB. Figures are anchored estimates, not measurements, we flag that on every row.

Relative performance: where the NVIDIA GeForce RTX 2080 Founders Edition lands

100% = this card, AI & Machine Learning headline metric (AI Score). #46 of 61 desktop cards in this vertical.

GPU	%	AI Score
NVIDIA GeForce RTX 5060	105%	2.1
Intel Arc B580	105%	2.1
NVIDIA GeForce RTX 2070 SUPER	100%	2
NVIDIA GeForce RTX 2070	100%	2
NVIDIA GeForce RTX 2080 Founders Edition	100%	2
NVIDIA GeForce RTX 3060 Ti	100%	2
Intel Arc A770 Limited Edition	100%	2
NVIDIA GeForce RTX 2060 Super	95%	1.9
GeForce RTX 4060	95%	1.9

One tier up: NVIDIA GeForce RTX 2070 (0%)
One tier down: NVIDIA GeForce RTX 3060 Ti (0%)
Best value alternative ($179): Intel Arc B580 (+5%)

Same card, other workloads: NVIDIA GeForce RTX 2080 Founders Edition Gaming benchmarks

← 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.

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), Full codebase review (needs Qwen2.5-Coder 14B).