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

16GB · AI Score 4.3/100 · first-party measured on 12 AI workloads

4.3 AI Score ✓ Measured

Every number on this page is first-party: NVIDIA GeForce RTX 4070 Ti Super 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 Ti Super delivers about 119.63 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 16GB. For image generation, SDXL runs at 4.45 it/s, while FLUX.1-dev won't fit at BF16 (needs ~26GB). 6 of the 12 workloads won't fit on 16GB at the tested precision, Qwen3 32B, Llama 3.3 70B, FLUX.1-dev, FLUX.1 Kontext 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 16GB actually changes this card's story: only 6 of 12 workloads gated instead of 8, and it never got near its 285W rating (I peaked it at 250W). The Z-Image and LTX video runs were my noisiest on this card, which makes sense, those sit right at the VRAM edge. 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 5

Qwen3 4B187.75

Llama 3.1 8B119.63

Qwen2.5-Coder 14B65.89

Workload	Result	Telemetry	Data
Qwen3 4B	187.75 tok/s	2.7 GB peak159 W59°C1.18 tok/WQ4_K_M	✓ Measured
Llama 3.1 8B	119.63 tok/s	4.6 GB peak191 W61°C0.63 tok/WQ4_K_M	✓ Measured
Qwen2.5-Coder 14B	65.89 tok/s	8.6 GB peak201 W65°C0.33 tok/WQ4_K_M	✓ Measured
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 3

Workload	Result	Telemetry	Data
Stable Diffusion XL	8.9 images/min	14.6 GB peak247 W69°C6.8 s/img	✓ Measured
Z-Image Turbo	1.65 images/min	14.1 GB peak126 W69°C37.1 s/img	✓ 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)	2.17 frames/s	9.2 GB peak152 W70°C44.8 s/clip	✓ 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 Ti Super specifications

Architecture	Ada Lovelace (AD103)
CUDA cores	8,448
VRAM	16GB GDDR6X
Memory bus	256-bit
Memory bandwidth	672 GB/s
Boost clock	2,610 MHz
TDP	285 W
Process	4nm
Interface	PCIe 4.0 x16
Release date	2024-01-24
Launch MSRP	$799

Verdict, capable, but 16GB sets the ceiling

NVIDIA GeForce RTX 4070 Ti Super scores 4.3/100, #51 of 102. It ran 6 of 12; 6 exceeded its 16GB. Every figure here is our own measurement.

Relative performance: where the NVIDIA GeForce RTX 4070 Ti Super lands

100% = this card, AI & Machine Learning headline metric (AI Score). #16 of 61 desktop cards in this vertical.

GPU	%	AI Score
GeForce RTX 5080	114%	4.9
NVIDIA GeForce RTX 4080	109%	4.7
GeForce RTX 5070 Ti	109%	4.7
NVIDIA RTX 4000 (Ada Generation)	102%	4.4
NVIDIA GeForce RTX 4070 Ti Super	100%	4.3
GeForce RTX 5060 Ti	86%	3.7
AMD Radeon RX 6950 XT	81%	3.5
NVIDIA GeForce RTX 3080 Ti	81%	3.5
AMD Radeon RX 9070 XT	79%	3.4

One tier up: NVIDIA RTX 4000 (Ada Generation) (+2%) · compare head-to-head
One tier down: GeForce RTX 5060 Ti (-14%) · compare head-to-head
Best value alternative ($749): GeForce RTX 5070 Ti (+9%) · compare head-to-head

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

← All AI & Machine Learning GPU rankings

The silicon

Transistors	45,900 million
Die size	378.6 mm²
Process node	4 nm
Fabricated by	TSMC
Transistor density	121.2 million per mm²

Denser than 87% 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	15.2 min	50.72 Wh	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).