Radeon RX 9070 vs GeForce RTX 4070 SUPER for Local AI
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Which GPU is better for local AI workloads?
The 9070's 16GB fits 14B-class LLMs at Q8 and modern image models without offloading; the 4070 SUPER's 12GB caps you at roughly 12B. Bandwidth is close (640 vs 504 GB/s, AMD ahead). The 4070 SUPER's advantages are CUDA compatibility and a 220W TDP versus 220W — equal power, actually.
Spec comparison: what actually differs
The table below is computed live from our hardware database. Positive deltas favor the Radeon RX 9070.
| Specification | Radeon RX 9070 | GeForce RTX 4070 SUPER | Difference |
|---|---|---|---|
| VRAM | 16 | 12 | +33% |
| Memory bandwidth | 640 | 504 | +27% |
| Memory type | GDDR6 | GDDR6X | — |
| Memory bus | 256 | 192 | — |
| TDP | 220 | 220 | — |
| CUDA cores | — | 7168 | — |
| Stream processors | 3584 | — | — |
| Architecture | RDNA 4 | Ada Lovelace | — |
| Launch MSRP | $549 | $599 | -8% |
| Street price | $549 | $599 | -8% |
What about price?
Which one should you buy for LLMs and image generation?
Radeon RX 9070
Buy the 9070 for model capacity and bandwidth per dollar.
Full specs & benchmarks →GeForce RTX 4070 SUPER
Buy the 4070 SUPER only if heavily discounted and your tools are CUDA-only.
Full specs & benchmarks →Is it faster for LLM inference?
The 9070 holds larger models and moves them faster (640 vs 504 GB/s). CUDA-only stacks still require NVIDIA.
How does it handle image generation?
SDXL is comfortable on both; Flux strongly favors the 9070's 16GB.
Which AI models fit on each card?
Computed from our model VRAM database at Q4 quantization: Radeon RX 9070 has 16 GB, GeForce RTX 4070 SUPER has 12 GB.
Fit only on the Radeon RX 9070 (16 GB)
Fit on both cards
Frequently asked questions
9070 vs 4070 SUPER for LLMs — which fits bigger models?
The 9070: 16GB versus 12GB means 14B models at Q8 load on AMD but not on the 4070 SUPER.
Which is faster at the same model?
The 9070 has higher memory bandwidth (640 vs 504 GB/s), so token generation is faster on models both cards can hold.