NVIDIA
NVIDIA GeForce RTX 4080 Super
16 GB · 736 GB/s
$979
Updated 2026-03-01
The NVIDIA GeForce RTX 4080 Super with 16 GB GDDR6X VRAM can handle 12 AI models across chat, coding, ai_coding. Best performance: Llama 3.2 1B Instruct at 200 tok/s (excellent). For AI coding workflows, it supports the Capable AI Coding tier — handles single model workflows well. Current price: approximately $979.
— OwnRig methodology, data updated 2026-03-01
Builder Capability: Capable AI Coding
Runs 16-22B coding models comfortably, or 32B at reduced quality. Handles single model workflows well.
Specifications
Memory
- Capacity
- 16 GB
- Bandwidth
- 736 GB/s
- Type
- GDDR6X
Power & Form Factor
- TDP
- 320W
- Rec. PSU
- 750W
- Slots
- 3-slot
- Length
- 304mm
Compatible Models
| Model | Quant | Speed | Rating | Notes |
|---|---|---|---|---|
| Llama 3.1 8B Instruct | Q8_0 | 82 tok/s | Excellent | 4080 Super (736 GB/s) between 4070 Ti and 4090. Full Q8 quality. |
| Qwen 2.5 Coder 32B Instruct | Q3_K_M | 18 tok/s | Acceptable | 16GB VRAM requires Q3 for 32B. Usable for code completion with quality compromise. |
| Mistral 7B Instruct v0.3 | Q8_0 | 78 tok/s | Excellent | Full Q8 7B. Strong performance on 16GB. |
| Phi-4 14B | Q5_K_M | 48 tok/s | Excellent | 14B Q5 fits on 16GB. Good reasoning speed. |
| Stable Diffusion XL 1.0 | FP16 | — | Excellent | ~4-6 seconds per 1024x1024 image. 16GB sufficient for SDXL. |
| Llama 3.2 3B Instruct | Q8_0 | 140 tok/s | Excellent | 736 GB/s bandwidth. 3B model runs at maximum speed. |
| Llama 3.2 1B Instruct | Q8_0 | 200 tok/s | Excellent | 1B model at maximum throughput. Ideal for latency-sensitive tasks. |
| Phi-4 Mini | Q8_0 | 130 tok/s | Excellent | 736 GB/s. Phi-4 mini at full Q8 quality. |
| Whisper Large V3 Turbo | FP16 | — | Excellent | Fast transcription. 736 GB/s bandwidth. |
| Stable Diffusion 3.5 Large | FP16 | — | Excellent | 736 GB/s. FP16 at 12.5GB. ~5s per image. Excellent 8.1B image model performance. |
| Gemma 3 27B | Q3_K_M | 14 tok/s | Acceptable | Q3_K_M fits. 736 GB/s bandwidth delivers acceptable throughput. |
| DeepSeek V3 | Q2_K | — | Not Viable | 671B MoE model requires 115GB+ at Q2_K. 16GB insufficient. Would need 128GB+ unified memory. |
Buy Used
Prices and availability vary. Inspect hardware before purchasing.
Frequently Asked Questions
- What AI models can NVIDIA GeForce RTX 4080 Super run?
- The NVIDIA GeForce RTX 4080 Super can run 12 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 4080 Super good for AI coding?
- Yes. With 16 GB, the NVIDIA GeForce RTX 4080 Super handles single-model coding workflows well at the Capable tier.
- How much VRAM does NVIDIA GeForce RTX 4080 Super have?
- The NVIDIA GeForce RTX 4080 Super has 16 GB of GDDR6X VRAM with 736 GB/s bandwidth.
- Can NVIDIA GeForce RTX 4080 Super run 70B models?
- 70B models can run on the NVIDIA GeForce RTX 4080 Super with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
- Is NVIDIA GeForce RTX 4080 Super worth it for AI?
- At $979, the NVIDIA GeForce RTX 4080 Super offers 16 GB VRAM and runs 12 AI models. It's a good entry point for smaller models and experimentation.
Generation: Ada Lovelace. Last updated: 2026-03-01.