NVIDIA
NVIDIA GeForce RTX 5080
16 GB · 960 GB/s
$1,099
Updated 2026-03-01
The NVIDIA GeForce RTX 5080 with 16 GB GDDR7 VRAM can handle 13 AI models across chat, coding, ai_coding. Best performance: Llama 3.2 1B Instruct at 230 tok/s (excellent). For AI coding workflows, it supports the Capable AI Coding tier — handles single model workflows well. Current price: approximately $1,099.
— 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
- 960 GB/s
- Type
- GDDR7
Power & Form Factor
- TDP
- 360W
- Rec. PSU
- 750W
- Slots
- 3-slot
- Length
- 304mm
Compatible Models
| Model | Quant | Speed | Rating | Notes |
|---|---|---|---|---|
| Llama 3.1 8B Instruct | Q8_0 | 92 tok/s | Excellent | RTX 5080 (960 GB/s) similar to 4090. Full Q8 quality. |
| Qwen 2.5 Coder 7B Instruct | Q8_0 | 88 tok/s | Excellent | Full Q8 coding model. Near-instant code completion. |
| Phi-4 14B | Q4_K_M | 52 tok/s | Excellent | 16GB VRAM fits 14B Q4. Good reasoning speed. |
| DeepSeek R1 Distill Qwen 7B | Q8_0 | 85 tok/s | Excellent | Full Q8 7B reasoning. Excellent on 5080. |
| Gemma 3 12B | Q5_K_M | 72 tok/s | Excellent | Q5 12B fits well. Strong performance on 5080. |
| Stable Diffusion XL 1.0 | FP16 | — | Excellent | ~3-5 seconds per 1024x1024 image. 16GB sufficient for SDXL. |
| Llama 3.2 3B Instruct | Q8_0 | 160 tok/s | Excellent | 960 GB/s bandwidth. Near-instant 3B inference. |
| Llama 3.2 1B Instruct | Q8_0 | 230 tok/s | Excellent | 960 GB/s. 1B model runs at near-maximum speed. |
| Phi-4 Mini | Q8_0 | 150 tok/s | Excellent | 960 GB/s. Near-instant 3.8B reasoning. |
| Whisper Large V3 Turbo | FP16 | — | Excellent | 960 GB/s. Low-latency transcription. |
| Stable Diffusion 3.5 Large | FP16 | — | Excellent | 960 GB/s. 16GB fits FP16. ~4.5s per image. |
| Gemma 3 27B | Q3_K_M | 18 tok/s | Acceptable | 16GB limits to Q3_K_M. Fast bandwidth (960 GB/s) helps speed but Q3 quality loss is noticeable. |
| 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 5080 run?
- The NVIDIA GeForce RTX 5080 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 5080 good for AI coding?
- Yes. With 16 GB, the NVIDIA GeForce RTX 5080 handles single-model coding workflows well at the Capable tier.
- How much VRAM does NVIDIA GeForce RTX 5080 have?
- The NVIDIA GeForce RTX 5080 has 16 GB of GDDR7 VRAM with 960 GB/s bandwidth.
- Can NVIDIA GeForce RTX 5080 run 70B models?
- 70B models can run on the NVIDIA GeForce RTX 5080 with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
- Is NVIDIA GeForce RTX 5080 worth it for AI?
- At $1,099, the NVIDIA GeForce RTX 5080 offers 16 GB VRAM and runs 13 AI models. It's a good entry point for smaller models and experimentation.
Generation: Blackwell. Last updated: 2026-03-01.