NVIDIA GeForce RTX 3050, AI & Machine Learning Benchmarks & Specs

8GB · AI Score 1.8/100 · anchored estimate vs 51 measured cards

1.8 AI Score Includes estimates

We have not run NVIDIA GeForce RTX 3050 on our bench. These figures are anchored estimates, interpolated per workload against the 51 GPUs we did measure (confidence: low). On Llama 3.1 8B (Q4_K_M) NVIDIA GeForce RTX 3050 should deliver about 41.4 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 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	63.7 tok/s	estimated	Est.
Llama 3.1 8B	41.4 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 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 (Bandwidth Theil-Sen ladder off NVIDIA anchors × vendor factor (diffusion slot corrected (below the measured RTX 3060)); measured VRAM floors; recalibrated 2026-09-19 against published llama.cpp (CUDA/ROCm/Vulkan/SYCL) and Stable Diffusion results.). The VRAM “won’t fit” gates are exact, since they’re pure capacity limits. Confidence: low. Estimates are replaced with measured data as more silicon goes through the bench. Full methodology →

NVIDIA GeForce RTX 3050 specifications

Architecture	Ampere (GA106)
CUDA cores	2,560
VRAM	8GB GDDR6
Memory bus	128-bit
Memory bandwidth	224 GB/s
Boost clock	1,777 MHz
TDP	130 W
Process	8nm
Interface	PCIe 4.0 x16
Release date	2022-01-27
Launch MSRP	$249

Verdict, capable, but 8GB sets the ceiling

NVIDIA GeForce RTX 3050 scores 1.8/100, #91 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 3050 lands

100% = this card, AI & Machine Learning headline metric (AI Score). #52 of 61 desktop cards in this vertical.

GPU	%	AI Score
Intel Arc A770 Limited Edition	111%	2
NVIDIA GeForce RTX 2060 Super	106%	1.9
GeForce RTX 4060	106%	1.9
NVIDIA GeForce RTX 5050	106%	1.9
NVIDIA GeForce RTX 3050	100%	1.8
NVIDIA GeForce GTX 1070 Ti	94%	1.7
NVIDIA GeForce GTX 1080	94%	1.7
AMD Radeon RX 6700	89%	1.6
AMD Radeon RX 7600	89%	1.6

One tier up: NVIDIA GeForce RTX 5050 (+6%)
One tier down: NVIDIA GeForce GTX 1070 Ti (-6%)
Best value alternative ($229): NVIDIA GeForce GTX 1660 Super (-11%)

Same card, other workloads: NVIDIA GeForce RTX 3050 Gaming benchmarks

← All AI & Machine Learning GPU rankings

The silicon

Transistors	12,000 million
Die size	276 mm²
Process node	8 nm
Fabricated by	Samsung
Transistor density	43.5 million per mm²

Denser than 78% 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).

Rental price

Renting this card costs $0.154/hour right now. We started tracking on 2026-10-03, so there is not enough history to chart yet. The line fills in as prices move.