Tesla K80 for AI in 2025: Too Old or Hidden Gem?
Last updated: December 2025
At $40-60 on eBay, the Tesla K80 seems like an incredible deal. Dual GPUs, 24GB total VRAM (12GB per GPU), straight from NVIDIA's datacenter lineup. Surely it can run some AI workloads?
TL;DR: Skip It
The K80 is from 2014 (Kepler architecture). It's so old that most modern AI software either doesn't support it or runs painfully slow. For $50 more, you can get vastly better options.
The Specs
| VRAM | 24GB total (2x 12GB GPUs) |
|---|---|
| Architecture | Kepler (2014) |
| CUDA Cores | 4992 (2x 2496) |
| Memory Bandwidth | 480 GB/s (combined) |
| TDP | 300W |
| Compute | FP32 only (8.7 TFLOPS combined) |
| Display Output | None |
| Cooling | Passive (requires server airflow) |
The Dual-GPU Problem
Here's what the eBay listings don't tell you: the K80 is two separate 12GB GPUs on one board, not a single 24GB GPU.
This matters because:
- Most LLM inference engines see them as two 12GB cards, not one 24GB card
- You can't easily pool the VRAM to load a single large model
- Multi-GPU inference is complex and often slower than a single faster GPU
So that "24GB" is really "12GB usable per model" in most scenarios.
Software Support: The Real Problem
Kepler is three generations behind the minimum for modern AI tooling:
- PyTorch: Dropped Kepler support in PyTorch 2.0
- ExLlama/ExLlama2: Requires Pascal (GTX 10 series) or newer
- Flash Attention: Not supported
- llama.cpp: Works, but FP32-only means half the speed
- CUDA 12: No Kepler support
You're stuck on CUDA 11.x with FP32 compute. Every modern optimization passes you by.
K80 vs. The Alternatives
| K80 | M40 | P40 | |
|---|---|---|---|
| Price (eBay) | $50 | $80 | $150 |
| Usable VRAM | 12GB* | 24GB | 24GB |
| Architecture | Kepler (2014) | Maxwell (2015) | Pascal (2016) |
| FP16 Support | No | No | Yes |
| Software Support | Limited | Adequate | Good |
| Real Speed (14B) | ~5 t/s | ~8 t/s | ~12 t/s |
*K80 is dual-GPU; can't combine VRAM for single model
When Does the K80 Make Sense?
Honestly? Almost never for AI in 2025. Maybe if:
- You already own one from a server decommission
- You're doing non-AI CUDA compute that doesn't need modern features
- You want to learn about multi-GPU setups (educational value only)
The Verdict: Don't Buy It
The K80 is a trap for AI beginners. The price looks amazing, but the 12GB-per-GPU limitation and ancient architecture make it nearly useless for modern LLM work.
The extra $100 for a P40 gets you true 24GB, FP16 support, 2x+ better performance, and software that actually works.
Get the Tesla P40 Instead
Same cooling requirements, same datacenter heritage, but actually usable:
- True 24GB unified VRAM
- Pascal architecture with FP16
- 2-3x faster inference
- Broad software support
Related
- Tesla P100 Review - 16GB HBM2 for ~$60, fast and cheap
- Tesla M40 Review - The $80 middle ground
- How we estimate inference speed