NVIDIA RTX 3080 — 10GB
Popular but 10GB VRAM is tight. Fine for 7B models, struggles beyond that.
Specifications
| Brand | NVIDIA |
|---|---|
| Model | RTX 3080 |
| VRAM | 10GB |
| Architecture | Ampere |
| CUDA / Stream Processors | 8,704 |
| Memory Bandwidth | 760 GB/s |
| TDP | 320W |
| FP32 TFLOPS | 30 |
Current Prices
Prices last updated:
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Price History
Best price dropped 6% since 2026-03-13
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For AI / LLM Use
Limited VRAM restricts you to 7B quantized models.
What Models Can It Run?
- 7B Q6_K, 14B Q3_K (tight)
- 7B Q4_K_M only
Estimated Performance
Generation: ~57 tokens/sec
Prefill: ~536 tokens/sec
Recommended Quantisations
- Q4_K_M for 7B models
- Q3_K for larger experiments
Pros & Cons
Pros
- Ampere architecture — good software support
- Consumer card — easy to install, display output
Cons
- Moderate memory bandwidth — not the fastest for inference
- 320W TDP — high power draw
Community Verdict
- r/LocalLLaMA
10GB is tight for AI. Can run 7B models but you will hit VRAM limits quickly with larger models.
Source