datacenter
Rent NVIDIA Tesla T4.
Turing
16GB VRAM
Price history
Daily median across providers.
Loading...
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 Tesla T4?
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 16GB of VRAM.
What's the VRAM minimum to run a model on the NVIDIA Tesla T4?
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 Tesla T4 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
INT4
INT4
FP16
FP16
INT4
INT8
INT8
INT8
INT4
INT4
Models that run on this GPU.
GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the NVIDIA Tesla T4.
DeepSeek R1 Distill Qwen 1.5B
by DeepSeek
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
2B
Context
128K tokens
Whisper Medium
by OpenAI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
1B
Context
30 tokens
Whisper Small
by OpenAI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
0B
Context
30 tokens
Whisper Base
by OpenAI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
0B
Context
30 tokens
Tiny — fits on a Raspberry Pi-class GPU.
Whisper Tiny
by OpenAI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
0B
Context
30 tokens
Llama 3.2 1B
by Meta AI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
1B
Context
128K tokens
Phone / browser deployable.
LFM2-24B-A2B
by Togethercomputer
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
24B
Context
33K tokens
Sarvam M
by Sarvamai
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
24B
Context
33K tokens
Whisper Large v3
by OpenAI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
2B
Context
30 tokens
Stable Diffusion 1.5
by Stability AI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
1B
?
INT4
TheDrummer: Cydonia 24B V4.1
by Thedrummer
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
24B
Context
131K tokens
Mistral: Mistral Small 3.2 24B
by Mistral AI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
24B
Context
128K tokens
DeepSeek R1 Distill Qwen 7B
by DeepSeek
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
8B
Context
128K tokens
Slow but workable on 16GB T4.
Llama 3.1 8B
by Meta AI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
8B
Context
128K tokens
Older T4s on a budget.
Mistral 7B v0.3
by Mistral AI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
7B
Context
33K tokens
Mistral: Mistral Small 3.1 24B
by Mistral AI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
24B
Context
128K tokens
Mistral: Mistral Small 3
by Mistral AI
Required GPUs
1× NVIDIA Tesla T4
Total VRAM
16 GB
Parameters
24B
Context
33K 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 Tesla T4?
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 16GB of VRAM.
What's the VRAM minimum to run a model on the NVIDIA Tesla T4?
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 Tesla T4 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.