NVIDIA GeForce RTX 4070
NVIDIA · 12GB GDDR6X · Can run 16 models
| Manufacturer | NVIDIA |
| VRAM | 12 GB |
| Memory Type | GDDR6X |
| Architecture | Ada Lovelace |
| CUDA Cores | 5,888 |
| Tensor Cores | 184 |
| TDP | 200W |
| MSRP | $599 |
| Released | Apr 13, 2023 |
AI Notes
The RTX 4070 is a popular entry point for local AI work. With 12GB of GDDR6X VRAM, it handles 7B models well and can run 13B models with Q4 quantization. Its low 200W TDP makes it efficient for sustained AI inference workloads in smaller builds.
Compatible Models
| Model | Parameters | Best Quant | VRAM Used | Fit |
|---|---|---|---|---|
| Llama 3.2 1B | 1B | Q8_0 | 3 GB | Runs |
| Gemma 2 2B | 2B | Q8_0 | 4 GB | Runs |
| Llama 3.2 3B | 3B | Q8_0 | 5 GB | Runs |
| Phi-3 Mini 3.8B | 3.8B | Q8_0 | 5.8 GB | Runs |
| DeepSeek R1 7B | 7B | Q8_0 | 9 GB | Runs |
| Mistral 7B | 7B | Q8_0 | 9 GB | Runs |
| Qwen 2.5 7B | 7B | Q8_0 | 9 GB | Runs |
| Qwen 2.5 Coder 7B | 7B | Q8_0 | 9 GB | Runs |
| Llama 3.1 8B | 8B | Q8_0 | 10 GB | Runs |
| DeepSeek R1 14B | 14B | Q4_K_M | 9.9 GB | Runs |
| Phi-4 14B | 14B | Q4_K_M | 9.9 GB | Runs |
| Qwen 2.5 14B | 14B | Q4_K_M | 9.9 GB | Runs |
| Gemma 2 9B | 9B | Q8_0 | 11 GB | Runs (tight) |
| StarCoder2 15B | 15B | Q8_0 | 17 GB | CPU Offload |
| Codestral 22B | 22B | Q4_K_M | 14.7 GB | CPU Offload |
| Gemma 2 27B | 27B | Q4_K_M | 17.7 GB | CPU Offload |
9
model(s) are too large for this hardware.