NVIDIA Titan RTX, AI & Machine Learning Benchmarks & Specs

24GB · AI Score 8.2/100 · first-party measured on 12 AI workloads

8.2 AI Score Includes estimates

Every number on this page is first-party: NVIDIA Titan RTX 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 Titan RTX delivers about 107.39 tokens/sec. Stepping up to Qwen3 32B it holds roughly 28.36 tok/s. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 24GB. For image generation, SDXL runs at 3.28 it/s, while FLUX.1-dev won't fit at BF16 (needs ~26GB). 6 of the 12 workloads won't fit on 24GB at the tested precision, Llama 3.3 70B, FLUX.1-dev, FLUX.1 Kontext, Qwen-Image-Edit 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 4B158.13

Llama 3.1 8B107.39

Qwen2.5-Coder 14B59.75

Qwen3 32B28.36

Workload	Result	Telemetry	Data
Qwen3 4B	158.13 tok/s	2.9 GB peak147 W67°C1.08 tok/WQ4_K_M	✓ Measured
Llama 3.1 8B	107.39 tok/s	4.8 GB peak159 W70°C0.68 tok/WQ4_K_M	✓ Measured
Qwen2.5-Coder 14B	59.75 tok/s	8.5 GB peak152 W75°C0.39 tok/WQ4_K_M	✓ Measured
Qwen3 32B	28.36 tok/s	18.6 GB peak121 W77°C0.24 tok/WQ4_K_M	✓ Measured
Llama 3.3 70B	✕ Won't fit needs ~46 GB	VRAM-gated at this precision	✓ Measured

Image Generation images/min 2

Workload	Result	Telemetry	Data
Stable Diffusion XL	6.56 images/min	12.7 GB peak273 W81°C9.2 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)	n/a frames/s	estimated	Est.
Wan 2.2 5B (720p)	n/a frames/s	estimated	Est.

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 Titan RTX specifications

Architecture	Turing (TU102)
CUDA cores	4,608
VRAM	24GB GDDR6
Memory bus	384-bit
Memory bandwidth	672 GB/s
Boost clock	1,770 MHz
TDP	280 W
Process	12nm
Interface	PCIe 3.0 x16
Release date	2018-12-18
Launch MSRP	$2,499

Verdict, capable, but 24GB sets the ceiling

NVIDIA Titan RTX scores 8.2/100, #32 of 102. It ran 5 of 12; 6 exceeded its 24GB. Every figure here is our own measurement.

Relative performance: where the NVIDIA Titan RTX lands

100% = this card, AI & Machine Learning headline metric (AI Score). #7 of 61 desktop cards in this vertical.

GPU	%	AI Score
NVIDIA RTX 5880 Ada Generation	199%	16.3
NVIDIA RTX 5000 Ada Generation	134%	11
NVIDIA GeForce RTX 4090	127%	10.4
NVIDIA GeForce RTX 3090 Ti	104%	8.5
NVIDIA Titan RTX	100%	8.2
NVIDIA GeForce RTX 3090	94%	7.7
AMD Radeon RX 7900 XTX	82%	6.7
AMD Radeon RX 7900 XT	76%	6.2
GeForce RTX 4080 Super	66%	5.4

One tier up: NVIDIA GeForce RTX 3090 Ti (+4%) · compare head-to-head
One tier down: NVIDIA GeForce RTX 3090 (-6%) · compare head-to-head
Best value alternative ($1,499): NVIDIA GeForce RTX 3090 (-6%) · compare head-to-head

Same card, other workloads: NVIDIA Titan RTX Gaming benchmarks

← All AI & Machine Learning GPU rankings

The silicon

Transistors	18,600 million
Die size	754 mm²
Fabricated by	TSMC
Transistor density	24.7 million per mm²

Denser than 28% 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	16.7 min	42.48 Wh	measured

Can't run: 40-product photo shoot (needs FLUX.1 Kontext dev), 6-panel comic page (needs FLUX.1 dev), 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), 100-photo restore and enlarge (needs FLUX.1 Kontext dev).