Louer Nvidia Nvidia Titan V.
3GB VRAM
250W
Where to rent it
All providers carrying this GPU.
No fresh prices yet.
FAQ
Frequently asked.
What AI models can I run on a Nvidia Titan V?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 3GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia Titan V?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
How does quantization (FP16 vs FP8 vs INT4) affect what fits?
Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.
Can I fine-tune on the Nvidia Titan V or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.
AI models
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP16
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
FP8
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
INT4
Models that run on this GPU.
GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia Nvidia Titan V.
Whisper Medium
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
30 tokens
Whisper Small
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
30 tokens
Whisper Base
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
30 tokens
Whisper Tiny
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
30 tokens
Stable Diffusion 1.5
by Stability AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Nomic Embed Text
by Nomic AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
8K tokens
mxbai-embed-large
by Mixedbread AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
512 tokens
BGE-M3
by BAAI (Beijing Academy of AI)
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
8K tokens
Gemma 3 1B
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
33K tokens
TinyLlama 1.1B
by TinyLlama Project
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
2K tokens
Gemma 3 270M It Lora
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
33K tokens
Meta Llama 3.2 1B Instruct
by Meta AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
131K tokens
Gemma 3 1b it
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
33K tokens
Gemma 3 270M It
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
33K tokens
Gemma 3 1B Pt
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
33K tokens
DeepSeek R1 Distill Qwen 1.5B
by DeepSeek
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
2B
Context
128K tokens
Whisper Large v3
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
2B
Context
30 tokens
Whisper Medium
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
30 tokens
Whisper Small
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
30 tokens
Whisper Base
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
30 tokens
Whisper Tiny
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
30 tokens
Stable Diffusion 1.5
by Stability AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Llama 3.2 1B
by Meta AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
128K tokens
Nomic Embed Text
by Nomic AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
8K tokens
mxbai-embed-large
by Mixedbread AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
512 tokens
BGE-M3
by BAAI (Beijing Academy of AI)
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
8K tokens
Gemma 3 1B
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
33K tokens
TinyLlama 1.1B
by TinyLlama Project
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
2K tokens
Moondream 1.8B
by Moondream
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
2B
Context
2K tokens
Gemma 3 270M It Lora
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
33K tokens
Gemma 2B It
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
2B
Context
8K tokens
Meta Llama 3.2 1B Instruct
by Meta AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
131K tokens
Gemma 3 1b it
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
33K tokens
Gemma 3 270M It
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
33K tokens
Gemma 3 1B Pt
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
33K tokens
Qwen3.5-2B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
2B
Context
262K tokens
Arcee AI: Spotlight
by Arcee Ai
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
131K tokens
DeepSeek R1 Distill Qwen 1.5B
by DeepSeek
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
2B
Context
128K tokens
Whisper Large v3
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
2B
Context
30 tokens
Whisper Medium
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
30 tokens
Whisper Small
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
30 tokens
Whisper Base
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
30 tokens
Whisper Tiny
by OpenAI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
30 tokens
Stable Diffusion 3.5 Medium
by Stability AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
3B
Stable Diffusion XL
by Stability AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
4B
Stable Diffusion 1.5
by Stability AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Llama 3.2 1B
by Meta AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
128K tokens
Llama 3.2 3B
by Meta AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
3B
Context
128K tokens
Nomic Embed Text
by Nomic AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
8K tokens
mxbai-embed-large
by Mixedbread AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
512 tokens
BGE-M3
by BAAI (Beijing Academy of AI)
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
8K tokens
Qwen 2.5 3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
3B
Context
33K tokens
Qwen 3 4B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
4B
Context
33K tokens
Gemma 2 2B
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
3B
Context
8K tokens
Gemma 3 4B
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
4B
Context
128K tokens
Gemma 3 1B
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
33K tokens
Phi-3.5 Mini
by Microsoft
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
4B
Context
128K tokens
Phi-3 Mini
by Microsoft
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
4B
Context
128K tokens
TinyLlama 1.1B
by TinyLlama Project
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
2K tokens
Moondream 1.8B
by Moondream
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
2B
Context
2K tokens
Mistral: Ministral 3 3B 2512
by Mistral AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
3B
Context
131K tokens
Qwen3 4B Base
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
4B
Context
33K tokens
Gemma 3 4b it
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
4B
Context
66K tokens
Gemma 3 270M It Lora
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
33K tokens
Qwen2.5 3B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
3B
Context
33K tokens
Gemma 2B It
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
2B
Context
8K tokens
Qwen3 4B Instruct 2507
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
4B
Context
262K tokens
Meta Llama 3.2 1B Instruct
by Meta AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
131K tokens
Gemma 3 1b it
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
33K tokens
Gemma 3 270M It
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
0B
Context
33K tokens
Meta Llama 3.2 3B Instruct
by Meta AI
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
3B
Context
131K tokens
Gemma 3 1B Pt
by Google DeepMind
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
33K tokens
Qwen3.5-2B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
2B
Context
262K tokens
Qwen3.5-4B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
4B
Context
262K tokens
Microsoft: Phi 4 Mini Instruct
by Microsoft
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
4B
Context
131K tokens
Arcee AI: Spotlight
by Arcee Ai
Required GPUs
1× Nvidia Titan V
Total VRAM
3 GB
Parameters
1B
Context
131K tokens
Renting for inference?
Pair these models with the cheapest provider on the rental table.
See rental rates on the Overview tab →
FAQ
AI models on this GPU.
What AI models can I run on a Nvidia Titan V?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 3GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia Titan V?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
How does quantization (FP16 vs FP8 vs INT4) affect what fits?
Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.
Can I fine-tune on the Nvidia Titan V or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.