NVIDIA Tesla K80 - 24GB
Looks like 24GB but it is two 12GB GPUs. Kepler is too old for most AI frameworks. Avoid unless it is nearly free.
Specifications
| Brand | NVIDIA |
|---|---|
| Model | Tesla K80 |
| VRAM | 24GB |
| Architecture | Kepler |
| CUDA / Stream Processors | 4,992 |
| Memory Bandwidth | 480 GB/s |
| TDP | 300W |
| FP32 TFLOPS | 8.7 |
Current Prices
Prices last updated:
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Price History
Best price dropped 37% since 2026-03-13
eBay
For AI / LLM Use
Solid choice for 30B models and comfortable 14B inference. Older architecture may have limited software support (check CUDA compatibility). Datacenter card with no display output, may need aftermarket cooling.
What Models Can It Run?
- 30B Q4_K_M, 14B full precision, 70B Q2 (tight)
- 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: ~36 tokens/sec
Prefill: ~155 tokens/sec
Recommended Quantisations
- Q4_K_M recommended for 30B models
- Q6_K or Q8 for 14B and below
- Full precision for 7B
Pros & Cons
Pros
- 24GB VRAM: handles large models
Cons
- Moderate memory bandwidth: not the fastest for inference
- 300W TDP: high power draw
- Older architecture: check CUDA/ROCm compatibility
- No display output: headless only
- May need aftermarket cooling solution
Community Verdict
- r/LocalLLaMA
Avoid. Dual-GPU means 12GB per die, Kepler lacks modern CUDA support, and it draws 300W. Buy a P40 instead.
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