NVIDIA
NVIDIA GeForce RTX 4070 Super
12 GB · 504 GB/s
$599
Updated 2026-03-01
The NVIDIA GeForce RTX 4070 Super with 12 GB GDDR6X VRAM can handle 13 AI models across chat, coding, ai_coding. Best performance: Llama 3.2 1B Instruct at 170 tok/s (excellent). For AI coding workflows, it supports the Starter AI Coding tier — good for 7-8B models. Current price: approximately $599.
— OwnRig methodology, data updated 2026-03-01
Builder Capability: Starter AI Coding
Runs 7-8B models comfortably. Good for basic local code completion and small model experiments.
Specifications
Memory
- Capacity
- 12 GB
- Bandwidth
- 504 GB/s
- Type
- GDDR6X
Power & Form Factor
- TDP
- 220W
- Rec. PSU
- 650W
- Slots
- 3-slot
- Length
- 244mm
Compatible Models
| Model | Quant | Speed | Rating | Notes |
|---|---|---|---|---|
| Llama 3.1 8B Instruct | Q5_K_M | 55 tok/s | Excellent | 4070 Super (504 GB/s) slightly slower than 4070 Ti Super. Q5 fits with headroom. |
| Qwen 2.5 7B Instruct | Q5_K_M | 52 tok/s | Excellent | 7B at Q5. Good value 12GB GPU for 7B class. |
| Mistral 7B Instruct v0.3 | Q5_K_M | 50 tok/s | Excellent | 7B at Q5. Solid everyday performance on 12GB. |
| Phi-3 Mini 3.8B Instruct | Q8_0 | 95 tok/s | Excellent | 3.8B at Q8. Overkill speed for this model size. |
| Gemma 3 4B | Q8_0 | 85 tok/s | Excellent | 4B at Q8. Minimal VRAM, fast inference. |
| nomic-embed-text v1.5 | FP16 | — | Excellent | 0.5GB VRAM. Runs alongside any model with negligible impact. |
| Llama 3.2 3B Instruct | Q8_0 | 110 tok/s | Excellent | 504 GB/s delivers excellent 3B speed. Full Q8 quality on 12GB. |
| Llama 3.2 1B Instruct | Q8_0 | 170 tok/s | Excellent | 1B at full Q8. Extremely fast on 12GB GPU. |
| Phi-4 Mini | Q8_0 | 100 tok/s | Excellent | 504 GB/s. 3.8B reasoning model runs well on 12GB. |
| Whisper Large V3 Turbo | FP16 | — | Excellent | Real-time audio transcription. 504 GB/s bandwidth. |
| Stable Diffusion 3.5 Large | Q8_0 | — | Good | FP16 at 12.5GB doesn't fit on 12GB. Q8_0 (9GB) fits. ~8s per image. |
| Gemma 3 27B | Q3_K_M | — | Not Viable | 12GB VRAM insufficient. Q3_K_M needs 13.3GB. Would need 16GB+ device. |
| DeepSeek V3 | Q2_K | — | Not Viable | 671B MoE model requires 115GB+ at Q2_K. 12GB insufficient. Would need 128GB+ unified memory. |
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Frequently Asked Questions
- What AI models can NVIDIA GeForce RTX 4070 Super run?
- The NVIDIA GeForce RTX 4070 Super can run 13 AI models. Top performers include Llama 3.2 1B Instruct, Llama 3.2 3B Instruct, Phi-4 Mini. See the full compatibility table above for speeds and quality ratings.
- Is NVIDIA GeForce RTX 4070 Super good for AI coding?
- With 12 GB, the NVIDIA GeForce RTX 4070 Super runs 7-8B coding models at the Starter tier. Good for basic code completion.
- How much VRAM does NVIDIA GeForce RTX 4070 Super have?
- The NVIDIA GeForce RTX 4070 Super has 12 GB of GDDR6X VRAM with 504 GB/s bandwidth.
- Can NVIDIA GeForce RTX 4070 Super run 70B models?
- 70B models can run on the NVIDIA GeForce RTX 4070 Super with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
- Is NVIDIA GeForce RTX 4070 Super worth it for AI?
- At $599, the NVIDIA GeForce RTX 4070 Super offers 12 GB VRAM and runs 13 AI models. It's a good entry point for smaller models and experimentation.
Generation: Ada Lovelace. Last updated: 2026-03-01.