NVIDIA TITAN V, AI & Machine Learning Benchmarks & Specs

12GB · AI Score 2.9/100 · anchored estimate vs 51 measured cards

2.9 AI Score Includes estimates

We have not run NVIDIA TITAN V 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 V should deliver about 104.1 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 2.22 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 4B154.8
Llama 3.1 8B104.1
Qwen2.5-Coder 14B54.3
WorkloadResultTelemetryData
Qwen3 4B154.8 tok/sestimatedEst.
Llama 3.1 8B104.1 tok/sestimatedEst.
Qwen2.5-Coder 14B54.3 tok/sestimatedEst.
Qwen3 32B✕ Won't fit VRAM-gated at this precisionEst.
Llama 3.3 70B✕ Won't fit VRAM-gated at this precisionEst.

Image Generation images/min 3

WorkloadResultTelemetryData
Stable Diffusion XL4.44 images/minestimatedEst.
Z-Image Turbo✕ Won't fit VRAM-gated at this precisionEst.
FLUX.1 dev✕ Won't fit VRAM-gated at this precisionEst.

Image Editing images/min 2

WorkloadResultTelemetryData
FLUX.1 Kontext dev✕ Won't fit VRAM-gated at this precisionEst.
Qwen-Image-Edit✕ Won't fit VRAM-gated at this precisionEst.

Video Generation frames/s 2

WorkloadResultTelemetryData
LTX-Video (distilled)✕ Won't fit VRAM-gated at this precisionEst.
Wan 2.2 5B (720p)✕ Won't fit VRAM-gated at this precisionEst.
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 TITAN V specifications

ArchitectureVolta (GV100)
CUDA cores5,120
VRAM12GB HBM2
Memory bus3072-bit
Memory bandwidth652.8 GB/s
Boost clock1,455 MHz
TDP250 W
Process12nm
InterfacePCIe 3.0 x16
Release date2017-12-07
Launch MSRP$2,999

Verdict, capable, but 12GB sets the ceiling

NVIDIA TITAN V scores 2.9/100, #71 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 V lands

100% = this card, AI & Machine Learning headline metric (AI Score). #32 of 61 desktop cards in this vertical.

GPURelative%AI Score
NVIDIA GeForce RTX 4070 Super
107%3.1
NVIDIA GeForce RTX 4070
107%3.1
NVIDIA GeForce RTX 4070 Ti
103%3
Intel Arc A770 Limited Edition
100%2.9
NVIDIA TITAN V
100%2.9
GeForce GTX 1080 Ti
90%2.6
NVIDIA GeForce RTX 3060
90%2.6
NVIDIA TITAN Xp
90%2.6
AMD Radeon RX 7700 XT
86%2.5

Same card, other workloads: NVIDIA TITAN V Gaming benchmarks

← All AI & Machine Learning GPU rankings

What this card can build

Whole-job timings, composed from our measured per-model results on this card.

WorkflowTimeEnergyBasis
Full codebase review18.4 minn/aestimate, 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).