NVIDIA RTX 4080 - 16GB usable
Great performance but 16GB limits you to 14B models. Consider 3090 for more VRAM at lower cost.
Specifications
| Brand | NVIDIA |
|---|---|
| Model | RTX 4080 |
| Usable VRAM | 16GB |
| Architecture | Ada |
| CUDA / Stream Processors | 9,728 |
| Memory Bandwidth | 717 GB/s |
| TDP | 320W |
| FP32 TFLOPS | 49 |
Current Offers
Used from $820New from $1200
Prices last updated:
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Price History
- eBay$820mid-range
- Amazon$1099current
- Newegg$1562at high
For AI / LLM Use
Good for 14B models. 30B requires aggressive quantization.
What Models Can It Run?
- 14B Q6_K, 30B Q3_K (tight)
- 14B Q4_K_M, 7B full precision
- 7B Q6_K, 14B Q3_K (tight)
- 7B Q4_K_M only
Estimated Performance
Generation: ~54 tokens/sec
Prefill: ~875 tokens/sec
Recommended Quantisations
- Q4_K_M for 14B models
- Q6_K for 7B-8B models
- Q8 for 7B if VRAM allows
Pros & Cons
Pros
- Ada architecture: broad CUDA software support
- Consumer card: easy to install, display output
Cons
- 16GB usable VRAM: may need quantization for 30B+ models
- Moderate memory bandwidth: not the fastest for inference
- 320W TDP: high power draw
Community Verdict
- r/LocalLLaMA
Fast but 16GB is the bottleneck. Most recommend spending the same money on a 3090 for more VRAM.
Source