NVIDIA GeForce RTX 4070 Ti
NVIDIA · 12GB GDDR6X · Can run 40 models
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| Manufacturer | NVIDIA |
| VRAM | 12 GB |
| Memory Type | GDDR6X |
| Architecture | Ada Lovelace |
| CUDA Cores | 7,680 |
| Tensor Cores | 240 |
| Bandwidth | 504 GB/s |
| TDP | 285W |
| MSRP | $799 |
| Released | Jan 5, 2023 |
AI Notes
The RTX 4070 Ti provides solid AI inference capability with 12GB of GDDR6X VRAM. It can run 7B-parameter models at full precision and 13B models with quantization. The 12GB VRAM limit means larger models require aggressive quantization or offloading to system RAM.
Compatible Models
| Model | Parameters | Best Quant | VRAM Used | Fit | Est. Speed |
|---|---|---|---|---|---|
| Qwen 3 0.6B | 600M | Q4_K_M | 2.5 GB | Runs | ~202 tok/s |
| Gemma 3 1B | 1B | Q8_0 | 2 GB | Runs | ~252 tok/s |
| Llama 3.2 1B | 1B | Q8_0 | 3 GB | Runs | ~168 tok/s |
| DeepSeek R1 1.5B | 1.5B | Q8_0 | 3 GB | Runs | ~168 tok/s |
| Gemma 2 2B | 2B | Q8_0 | 4 GB | Runs | ~126 tok/s |
| Gemma 3n E2B | 2B | Q4_K_M | 3.3 GB | Runs | ~153 tok/s |
| Llama 3.2 3B | 3B | Q8_0 | 5 GB | Runs | ~101 tok/s |
| Phi-3 Mini 3.8B | 3.8B | Q8_0 | 5.8 GB | Runs | ~87 tok/s |
| Phi-4 Mini 3.8B | 3.8B | Q4_K_M | 4.5 GB | Runs | ~112 tok/s |
| Gemma 3 4B | 4B | Q4_K_M | 5 GB | Runs | ~101 tok/s |
| Gemma 3n E4B | 4B | Q4_K_M | 4.5 GB | Runs | ~112 tok/s |
| Qwen 3 4B | 4B | Q4_K_M | 4.5 GB | Runs | ~112 tok/s |
| DeepSeek R1 7B | 7B | Q8_0 | 9 GB | Runs | ~56 tok/s |
| Falcon 3 7B | 7B | Q4_K_M | 6.8 GB | Runs | ~74 tok/s |
| Mistral 7B | 7B | Q8_0 | 9 GB | Runs | ~56 tok/s |
| Qwen 2.5 7B | 7B | Q8_0 | 9 GB | Runs | ~56 tok/s |
| Qwen 2.5 Coder 7B | 7B | Q8_0 | 9 GB | Runs | ~56 tok/s |
| Qwen 2.5 VL 7B | 7B | Q4_K_M | 7 GB | Runs | ~72 tok/s |
| Cogito 8B | 8B | Q4_K_M | 7.5 GB | Runs | ~67 tok/s |
| DeepSeek R1 8B | 8B | Q4_K_M | 7.5 GB | Runs | ~67 tok/s |
| Llama 3.1 8B | 8B | Q8_0 | 10 GB | Runs | ~50 tok/s |
| Nemotron 3 Nano 8B | 8B | Q4_K_M | 7.5 GB | Runs | ~67 tok/s |
| Qwen 3 8B | 8B | Q4_K_M | 7.5 GB | Runs | ~67 tok/s |
| Falcon 3 10B | 10B | Q4_K_M | 8.5 GB | Runs | ~59 tok/s |
| Llama 3.2 Vision 11B | 11B | Q4_K_M | 8.5 GB | Runs | ~59 tok/s |
| Mistral Nemo 12B | 12B | Q4_K_M | 9.5 GB | Runs | ~53 tok/s |
| DeepSeek R1 14B | 14B | Q4_K_M | 9.9 GB | Runs | ~51 tok/s |
| Phi-4 14B | 14B | Q4_K_M | 9.9 GB | Runs | ~51 tok/s |
| Qwen 2.5 14B | 14B | Q4_K_M | 9.9 GB | Runs | ~51 tok/s |
| Gemma 2 9B | 9B | Q8_0 | 11 GB | Runs (tight) | ~46 tok/s |
| Gemma 3 12B | 12B | Q4_K_M | 10.5 GB | Runs (tight) | ~48 tok/s |
| Phi-4 Reasoning 14B | 14B | Q4_K_M | 11 GB | Runs (tight) | ~46 tok/s |
| Qwen 2.5 Coder 14B | 14B | Q4_K_M | 12 GB | CPU Offload | ~13 tok/s |
| Qwen 3 14B | 14B | Q4_K_M | 12 GB | CPU Offload | ~13 tok/s |
| StarCoder2 15B | 15B | Q8_0 | 17 GB | CPU Offload | ~9 tok/s |
| Codestral 22B | 22B | Q4_K_M | 14.7 GB | CPU Offload | ~10 tok/s |
| Devstral 24B | 24B | Q4_K_M | 17 GB | CPU Offload | ~9 tok/s |
| Magistral Small 24B | 24B | Q4_K_M | 17 GB | CPU Offload | ~9 tok/s |
| Mistral Small 3.1 24B | 24B | Q4_K_M | 18 GB | CPU Offload | ~8 tok/s |
| Gemma 2 27B | 27B | Q4_K_M | 17.7 GB | CPU Offload | ~8 tok/s |
29
model(s) are too large for this hardware.