GeForce RTX 5070, AI & Machine Learning Benchmarks & Specs

12GB · AI Score 3.2/100 · first-party measured on 12 AI workloads

3.2 AI Score Includes estimates

Every number on this page is first-party: GeForce RTX 5070 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) GeForce RTX 5070 delivers about 119.87 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 12GB. For image generation, SDXL runs at 3.03 it/s, while FLUX.1-dev won't fit at BF16 (needs ~26GB). 9 of the 12 workloads won't fit on 12GB 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.

Bench notes: from the person who ran it

The 5070 pulled 101% of its 250W rating on Llama 8B. It will use every watt. The problem is the 12GB: 8 of 12 workloads gated, same wall as the 4070. One coder-14B result came back as a fail it shouldn't have been, so that's flagged pending a re-run. Quick note on the setup: all my AI benchmarking was done on rented cloud GPUs, I used all three of Vast.ai, RunPod and Modal depending on which had the card, and they all have their pros and cons. Same pinned harness on every run, and everything here got double-checked before it went up.

AI & Machine Learning benchmark results

Text Generation tok/s 19

MiniCPM5 2B275.74

LFM2.5 2.6B266.12

Nemotron 3 Nano 4B184.99

Granite 4.1 3B183.95

Qwen3 4B180.21

DeepSeek Coder 7B Instruct v1.5126.75

Llama 3.1 8B119.87

Llama 3 8B112.34

Qwen3 8B109.63

Ornith 1.5 9B99.41

Nemotron Nano 9B v286.81

Gemma 4 12B69.06

Workload	Result	Telemetry	Data
MiniCPM5 2B	275.74 tok/s	82 W57°CQ4_K_M	✓ Measured
LFM2.5 2.6B	266.12 tok/s	103 W60°CQ4_K_M	✓ Measured
Granite 4.1 3B	183.95 tok/s	111 W60°CQ4_K_M	✓ Measured
Nemotron 3 Nano 4B	184.99 tok/s	106 W58°CQ4_K_M	✓ Measured
Qwen3 4B	180.21 tok/s	2.6 GB peak132 W48°C1.36 tok/WQ4_K_M	✓ Measured
DeepSeek Coder 7B Instruct v1.5	126.75 tok/s	138 W61°CQ4_K_M	✓ Measured
Llama 3 8B	112.34 tok/s	145 W66°CQ4_K_M	✓ Measured
Llama 3.1 8B	119.87 tok/s	4.6 GB peak210 W53°C0.57 tok/WQ4_K_M	✓ Measured
Qwen3 8B	109.63 tok/s	140 W61°CQ4_K_M	✓ Measured
Nemotron Nano 9B v2	86.81 tok/s	126 W60°CQ4_K_M	✓ Measured
Ornith 1.5 9B	99.41 tok/s	143 W65°CQ4_K_M	✓ Measured
Gemma 4 12B	69.06 tok/s	143 W63°CQ4_K_M	✓ Measured
Qwen2.5-Coder-14B	58.15 tok/s	159 W68°CQ4_K_M	✓ Measured
Qwen3 14B	60.87 tok/s	159 W68°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 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 10

SD Turbo535.06

SDXL Turbo383.37

LCM DreamShaper v7142.36

Stable Diffusion 1.539.2

Sana 1.6B19.71

Stable Diffusion XL7.43

Playground v2.54.37

Workload	Result	Telemetry	Data
Stable Diffusion 1.5	39.2 images/min	181 W66°C	✓ Measured
SD Turbo	535.06 images/min	62 W68°C	✓ Measured
LCM DreamShaper v7	142.36 images/min	144 W71°C	✓ Measured
SDXL Turbo	383.37 images/min	47 W52°C	✓ Measured
Stable Diffusion XL	7.43 images/min	233 W83°C	✓ Measured
Sana 1.6B	19.71 images/min	209 W68°C	✓ Measured
Playground v2.5	4.37 images/min	180 W72°C	✓ Measured
Z-Image Turbo	✕ Won't fit needs ~13 GB	VRAM-gated at this precision	✓ Measured
PixArt-Sigma XL	✕ Won't fit needs ~14 GB	VRAM-gated at this precision	✓ 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 2

Workload	Result	Telemetry	Data
LTX-Video (distilled)	✕ Won't fit needs ~14 GB	VRAM-gated at this precision	✓ Measured
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.

GeForce RTX 5070 specifications

Architecture	Blackwell
CUDA cores	6,144
VRAM	12GB GDDR7
Memory bus	192-bit
Memory bandwidth	672 GB/s
Boost clock	2,512 MHz
TDP	250 W
Process	4nm
Interface	PCIe 4.0 x16
Release date	2025-03-05
Launch MSRP	$549

Verdict, capable, but 12GB sets the ceiling

GeForce RTX 5070 scores 3.2/100, #55 of 102. It ran 3 of 12; 9 exceeded its 12GB. Every figure here is our own measurement.

Relative performance: where the GeForce RTX 5070 lands

100% = this card, AI & Machine Learning headline metric (AI Score). #20 of 61 desktop cards in this vertical.

GPU	%	AI Score
GeForce RTX 5060 Ti	116%	3.7
NVIDIA GeForce RTX 3080 Ti	109%	3.5
NVIDIA GeForce RTX 4060 Ti 16GB	100%	3.2
NVIDIA GeForce RTX 4070 Ti	100%	3.2
GeForce RTX 5070	100%	3.2
NVIDIA GeForce RTX 4070 Super	97%	3.1
NVIDIA GeForce RTX 4070	97%	3.1
NVIDIA TITAN V	91%	2.9
NVIDIA GeForce RTX 3060	81%	2.6

One tier up: NVIDIA GeForce RTX 4070 Ti (0%) · compare head-to-head
One tier down: NVIDIA GeForce RTX 4070 Super (-3%) · compare head-to-head
Best value alternative ($499): NVIDIA GeForce RTX 4060 Ti 16GB (0%) · compare head-to-head

Same card, other workloads: GeForce RTX 5070 Gaming benchmarks

← All AI & Machine Learning GPU rankings

The silicon

Transistors	31,100 million
Die size	263 mm²
Process node	4 nm
Fabricated by	TSMC
Transistor density	118.3 million per mm²

Denser than 92% 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	17.2 min	45.69 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 $549 to buy. The cheapest listed rate on Vast.ai is $0.162/hour, but that is the floor: we budget $0.194/hour, a 20% premium, because idle time, storage and unavailable cheap instances all land on the same bill. At that rate buying wins after 2,824 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	$142	3.9 years
8 hours a day, working on it	2,920	$568	11.6 months
24/7, always-on agent	8,760	$1,703	3.9 months

At steady usage this card pays for itself inside a normal ownership window. 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

$0.162/hr+12.5% since 2026-09-30low $0.144 · high $0.216

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