NVIDIA GeForce RTX 4070, AI & Machine Learning Benchmarks & Specs

12GB · AI Score 3.1/100 · first-party measured on 12 AI workloads

3.1 AI Score Includes estimates

Every number on this page is first-party: NVIDIA GeForce RTX 4070 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 GeForce RTX 4070 delivers about 93.16 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 12GB. 8 of the 12 workloads won't fit on 12GB at the tested precision, Qwen3 32B, Llama 3.3 70B, Z-Image Turbo, FLUX.1-dev and others. We publish those as hard gates rather than quietly dropping to a smaller quant.

Bench notes: from the person who ran it

Most efficient card in its class that I measured, 1.36 tokens/watt on Qwen3 4B, and it never broke 57°C on anything. But the 12GB ceiling is real: 8 of my 12 workloads simply don't fit. Great small-LLM card, wrong card for image/video generation. 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 2B244.68

LFM2.5 2.6B230.17

Granite 4.1 3B170.86

Qwen3 4B149.68

Nemotron 3 Nano 4B145.5

DeepSeek Coder 7B Instruct v1.5105.21

Llama 3 8B93.2

Llama 3.1 8B93.16

Qwen3 8B90.94

Ornith 1.5 9B80.73

Nemotron Nano 9B v266.16

Gemma 4 12B58.51

Workload	Result	Telemetry	Data
MiniCPM5 2B	244.68 tok/s	89 W56°CQ4_K_M	✓ Measured
LFM2.5 2.6B	230.17 tok/s	95 W54°CQ4_K_M	✓ Measured
Granite 4.1 3B	170.86 tok/s	114 W60°CQ4_K_M	✓ Measured
Nemotron 3 Nano 4B	145.5 tok/s	120 W57°CQ4_K_M	✓ Measured
Qwen3 4B	149.68 tok/s	2.6 GB peak110 W53°C1.36 tok/WQ4_K_M	✓ Measured
DeepSeek Coder 7B Instruct v1.5	105.21 tok/s	136 W61°CQ4_K_M	✓ Measured
Llama 3 8B	93.2 tok/s	135 W60°CQ4_K_M	✓ Measured
Llama 3.1 8B	93.16 tok/s	4.8 GB peak125 W54°C0.75 tok/WQ4_K_M	✓ Measured
Qwen3 8B	90.94 tok/s	139 W61°CQ4_K_M	✓ Measured
Nemotron Nano 9B v2	66.16 tok/s	144 W60°CQ4_K_M	✓ Measured
Ornith 1.5 9B	80.73 tok/s	130 W60°CQ4_K_M	✓ Measured
Gemma 4 12B	58.51 tok/s	139 W61°CQ4_K_M	✓ Measured
Qwen2.5-Coder 14B	50.74 tok/s	8.6 GB peak131 W57°C0.39 tok/WQ4_K_M	✓ Measured
Qwen3 14B	51.46 tok/s	150 W61°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 8

SDXL Turbo286.04

Stable Diffusion 1.535.36

DreamShaper XL Turbo22.67

Sana 1.6B16.6

Stable Diffusion XL6.6

Playground v2.54.01

Workload	Result	Telemetry	Data
Stable Diffusion 1.5	35.36 images/min	173 W55°C	✓ Measured
SDXL Turbo	286.04 images/min	50 W39°C	✓ Measured
Sana 1.6B	16.6 images/min	196 W68°C	✓ Measured
Stable Diffusion XL	6.6 images/min	196 W69°C	✓ Measured
DreamShaper XL Turbo	22.67 images/min	195 W61°C	✓ Measured
Playground v2.5	4.01 images/min	197 W72°C	✓ Measured
Z-Image Turbo	✕ Won't fit needs ~13 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.

NVIDIA GeForce RTX 4070 specifications

Architecture	Ada Lovelace
CUDA cores	5,888
VRAM	12GB GDDR6X
Memory bus	192-bit
Memory bandwidth	504 GB/s
Boost clock	2,475 MHz
TDP	200 W
Process	4nm (TSMC 4N)
Interface	PCIe 4.0 x16
Release date	2023-04-13
Launch MSRP	$599

Verdict, capable, but 12GB sets the ceiling

NVIDIA GeForce RTX 4070 scores 3.1/100, #56 of 102. It ran 3 of 12; 8 exceeded its 12GB. Every figure here is our own measurement.

Relative performance: where the NVIDIA GeForce RTX 4070 lands

100% = this card, AI & Machine Learning headline metric (AI Score). #22 of 61 desktop cards in this vertical.

GPU	%	AI Score
NVIDIA GeForce RTX 4060 Ti 16GB	103%	3.2
NVIDIA GeForce RTX 4070 Ti	103%	3.2
GeForce RTX 5070	103%	3.2
NVIDIA GeForce RTX 4070 Super	100%	3.1
NVIDIA GeForce RTX 4070	100%	3.1
NVIDIA TITAN V	94%	2.9
NVIDIA GeForce RTX 3060	84%	2.6
AMD Radeon RX 7900 XT	81%	2.5
AMD Radeon RX 7800 XT	77%	2.4

One tier up: NVIDIA GeForce RTX 4070 Super (0%) · compare head-to-head
One tier down: NVIDIA TITAN V (-6%) · compare head-to-head
Best value alternative ($499): NVIDIA GeForce RTX 4060 Ti 16GB (+3%) · compare head-to-head

Same card, other workloads: NVIDIA GeForce RTX 4070 Gaming benchmarks

← All AI & Machine Learning GPU rankings

The silicon

Transistors	35,800 million
Die size	294.5 mm²
Process node	4 nm
Fabricated by	TSMC
Transistor density	121.6 million per mm²

Denser than 94% 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	19.7 min	43.13 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 $599 to buy. The cheapest listed rate on Vast.ai is $0.136/hour, but that is the floor: we budget $0.163/hour, a 20% premium, because idle time, storage and unavailable cheap instances all land on the same bill. At that rate buying wins after 3,670 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	$119	5.0 years
8 hours a day, working on it	2,920	$477	1.3 years
24/7, always-on agent	8,760	$1,430	5.0 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

$0.136/hr+65.9% since 2026-08-14low $0.069 · high $0.163

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