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

12GB · AI Score 3.0/100 · anchored estimate vs 51 measured cards

3 AI Score Includes estimates

We have not run NVIDIA GeForce RTX 4070 Ti 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 4070 Ti should deliver about 86.6 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 12GB. For image generation, SDXL should run near 4.21 it/s, while FLUX.1-dev won't fit at BF16 (needs ~26GB). 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.

AI & Machine Learning benchmark results

Text Generation tok/s 5

Qwen3 4B135

Llama 3.1 8B86.6

Qwen2.5-Coder 14B50.7

Workload	Result	Telemetry	Data
Qwen3 4B	135 tok/s	estimated	Est.
Llama 3.1 8B	86.6 tok/s	estimated	Est.
Qwen2.5-Coder 14B	50.7 tok/s	estimated	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	8.42 images/min	estimated	Est.
Z-Image Turbo	✕ Won't fit	VRAM-gated at this precision	Est.
FLUX.1 dev	✕ 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 4070 Ti specifications

Architecture	Ada Lovelace (AD104)
CUDA cores	7,680
VRAM	12GB GDDR6X
Memory bus	192-bit
Memory bandwidth	504 GB/s
Boost clock	2,610 MHz
TDP	285 W
Process	4nm
Interface	PCIe 4.0 x16
Release date	2023-01-05
Launch MSRP	$799

Verdict, capable, but 12GB sets the ceiling

NVIDIA GeForce RTX 4070 Ti scores 3.0/100, #68 of 102. It ran 4 of 12; 8 exceeded its 12GB. Figures are anchored estimates, not measurements, we flag that on every row.

Relative performance: where the NVIDIA GeForce RTX 4070 Ti lands

100% = this card, AI & Machine Learning headline metric (AI Score). #30 of 61 desktop cards in this vertical.

GPU	%	AI Score
NVIDIA GeForce RTX 4060 Ti 16GB	107%	3.2
GeForce RTX 5070	107%	3.2
NVIDIA GeForce RTX 4070 Super	103%	3.1
NVIDIA GeForce RTX 4070	103%	3.1
NVIDIA GeForce RTX 4070 Ti	100%	3
Intel Arc A770 Limited Edition	97%	2.9
NVIDIA TITAN V	97%	2.9
GeForce GTX 1080 Ti	87%	2.6
NVIDIA GeForce RTX 3060	87%	2.6

One tier up: NVIDIA GeForce RTX 4070 (+3%)
One tier down: Intel Arc A770 Limited Edition (-3%)
Best value alternative ($349): Intel Arc A770 Limited Edition (-3%)

Same card, other workloads: NVIDIA GeForce RTX 4070 Ti 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 91% of the 76 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	n/a	estimate, 0 of 1 stage measured

Can't run: 60-second AI short film (needs Qwen3 32B), 60-second AI short film, narrated (needs Qwen3 32B), 10 short social clips (needs Qwen3 32B), 40-product photo shoot (needs FLUX.1 Kontext dev), 6-panel comic page (needs Qwen3 32B), 20 long-form articles (needs Llama 3.3 70B), Character sheet, 12 poses (needs FLUX.1 dev), 100-photo restoration batch (needs FLUX.1 Kontext dev), 24-frame storyboard (needs Qwen3 32B), 100-photo restore and enlarge (needs FLUX.1 Kontext dev).

Rent or buy?

This card is $799 to buy. The cheapest listed rate on RunPod is $0.190/hour, but that is the floor: we budget $0.228/hour, a 20% premium, because idle time, storage and unavailable cheap instances all land on the same bill. At that rate buying wins after 3,504 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	$166	4.8 years
8 hours a day, working on it	2,920	$666	1.2 years
24/7, always-on agent	8,760	$1,997	4.8 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

$0.190/hr+0.0% since 2026-08-14low $0.190 · high $0.190

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