NVIDIA TITAN X (Pascal), AI & Machine Learning Benchmarks & Specs

12GB · AI Score 2.4/100 · anchored estimate vs 51 measured cards

2.4 AI Score Includes estimates

We have not run NVIDIA TITAN X (Pascal) 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 TITAN X (Pascal) should deliver about 61.6 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 0.82 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 8

Qwen3 4B100.1

Llama 3.1 8B61.6

Qwen2.5-Coder 14B30.5

Workload	Result	Telemetry	Data
Qwen3 4B	100.1 tok/s	estimated	Est.
Llama 3.1 8B	61.6 tok/s	estimated	Est.
Qwen2.5-Coder 14B	30.5 tok/s	estimated	Est.
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	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	1.65 images/min	estimated	Est.
FLUX.1 dev	✕ Won't fit	VRAM-gated at this precision	Est.
Z-Image Turbo	✕ 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 (Bandwidth Theil-Sen ladder off NVIDIA anchors × vendor factor (LLM ×0.65, diffusion ×0.3 (GP102, at the 1080 Ti)); measured VRAM floors; recalibrated 2026-09-19 against published llama.cpp (CUDA/ROCm/Vulkan/SYCL) and Stable Diffusion results.). 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 TITAN X (Pascal) specifications

Architecture	Pascal (GP102)
CUDA cores	3,584
VRAM	12GB GDDR5X
Memory bus	384-bit
Memory bandwidth	480 GB/s
Boost clock	1,531 MHz
TDP	250 W
Process	16nm
Interface	PCIe 3.0 x16
Release date	2016-08-02
Launch MSRP	$1,200

Verdict, capable, but 12GB sets the ceiling

NVIDIA TITAN X (Pascal) scores 2.4/100, #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 TITAN X (Pascal) lands

100% = this card, AI & Machine Learning headline metric (AI Score). #29 of 61 desktop cards in this vertical.

GPU	%	AI Score
AMD Radeon RX 7900 XT	104%	2.5
AMD Radeon RX 7800 XT	100%	2.4
AMD Radeon RX 9070 XT	100%	2.4
AMD Radeon RX 9070	100%	2.4
NVIDIA TITAN X (Pascal)	100%	2.4
AMD Radeon RX 6800 XT	96%	2.3
AMD Radeon RX 6800	96%	2.3
AMD Radeon RX 6900 XT	96%	2.3
AMD Radeon RX 6950 XT	96%	2.3

One tier up: AMD Radeon RX 9070 (0%)
One tier down: AMD Radeon RX 6800 XT (-4%)
Best value alternative ($329): NVIDIA GeForce RTX 3060 (+8%)

Same card, other workloads: NVIDIA TITAN X (Pascal) Gaming benchmarks

← All AI & Machine Learning GPU rankings

The silicon

Transistors	12,000 million
Die size	471 mm²
Process node	16 nm
Fabricated by	TSMC
Transistor density	25.5 million per mm²

Denser than 73% 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	32.8 min	n/a	estimate, 0 of 1 stage 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).