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NVIDIA Titan V

12 GB VRAM29.8 FP16 TFLOPS250 W TDP0 providers0 offerings0 regions

The NVIDIA Titan V is a consumer-grade GPU built on the Volta architecture, featuring 12 GB of memory with 653 GB/s of memory bandwidth and a 250 W TDP. It delivers 14.9 TFLOPS of FP32 performance and 29.8 TFLOPS of FP16 tensor performance.

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Hardware specifications

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FP16 performance
29.8 TFLOPS
peak
TDP
250 W
Memory bandwidth
653 GB/s
Architecture
Volta
FP32 performance
14.9 TFLOPS
Brand
NVIDIA
Series
Titan
Market price history

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LLMs that fit on the NVIDIA Titan V

Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 12 GB per GPU.

ModelParameters16-bit8-bit4-bit
Gemma 3 12B
Google
12.2B
29 GB
Too large
16 GB
Too large
9.4 GB
1× · 2.6 GB free
Granite 4.1 8B
IBM
8.8B
21 GB
Too large
12 GB
Barely fits on 1×
6.8 GB
1× · 5.2 GB free
Qwen3 8B
Alibaba
8.2B
20 GB
Too large
11 GB
1× · 1.2 GB free
6.3 GB
1× · 5.7 GB free
Llama 3.1 8B Instruct
Meta
8B
19 GB
Too large
11 GB
1× · 1.4 GB free
6.2 GB
1× · 5.8 GB free
Mistral 7B Instruct v0.3
Mistral AI
7.2B
17 GB
Too large
9.6 GB
1× · 2.4 GB free
5.6 GB
1× · 6.4 GB free
Qwen3 4B
Alibaba
4B
9.7 GB
1× · 2.3 GB free
5.3 GB
1× · 6.7 GB free
3.1 GB
1× · 8.9 GB free

Estimates: model weights plus 20% for KV cache and runtime overhead, at short context lengths. Long contexts and large batches need more. Rows marked “tight” hold the weights but not that full margin. A ×N figure is the total VRAM across N of these GPUs and makes no claim about interconnect throughput. How we estimate this

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Frequently asked questions

How much VRAM does the Titan V have?
The Titan V comes with 12 GB of VRAM, which determines the largest models and batch sizes it can hold in memory for training and inference.
Which LLMs can I run on the Titan V?
With 12 GB of VRAM, a single Titan V can serve models such as Gemma 3 12B, Granite 4.1 8B, Qwen3 8B, Llama 3.1 8B Instruct. The table above lists the estimated VRAM for each model at 16-bit, 8-bit and 4-bit precision, including the multi-GPU configurations needed for larger models.