consumer
Nvidia Nvidia RTX 4070 Ti mieten.
Ada Lovelace
12GB VRAM
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 RTX 4070 Ti?
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 12GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia RTX 4070 Ti?
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 RTX 4070 Ti 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
INT4
INT4
FP16
INT4
FP16
INT4
INT4
INT4
INT4
INT4
FP8
FP8
FP8
Models that run on this GPU.
GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia Nvidia RTX 4070 Ti.
Llama 4 Scout 17B 16E Instruct Fp8 Lora
by Meta AI
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
17B
Context
10.5M tokens
DeepSeek R1 Distill Qwen 7B
by DeepSeek
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
8B
Context
128K tokens
Stable Diffusion 3.5 Medium
by Stability AI
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
3B
Llama 4 Scout Instruct (17Bx16E)
by Meta AI
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
17B
Context
1M tokens
Stable Diffusion XL
by Stability AI
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
4B
Mistral 7B v0.3
by Mistral AI
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
7B
Context
33K tokens
Llama 4 Maverick Instruct (17Bx128E) FP8
by Meta AI
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
17B
Context
1M tokens
Llama 4 Scout (17Bx16E)
by Meta AI
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
17B
Context
262K tokens
DeepSeek Coder V2 Lite
by DeepSeek
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
16B (2B active)
Context
128K tokens
DeepSeek R1 Distill Qwen 14B
by DeepSeek
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
15B
Context
128K tokens
Gemma 2 9B
by Google DeepMind
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
9B
Context
8K tokens
Qwen: Qwen3.5-9B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
9B
Context
262K tokens
NVIDIA: Nemotron Nano 9B V2
by Nvidia
Required GPUs
1× Nvidia RTX 4070 Ti
Total VRAM
12 GB
Parameters
9B
Context
131K tokens
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FAQ
AI models on this GPU.
What AI models can I run on a Nvidia RTX 4070 Ti?
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 12GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia RTX 4070 Ti?
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 RTX 4070 Ti 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.