GPUDojo

NVIDIA Tesla K80 - 12GB usable

Avoid for modern local AI: its advertised 24GB is split across two independent 12GB GPUs.

Specifications

BrandNVIDIA
ModelTesla K80
Usable VRAM12GB (24GB split across the board)
ArchitectureKepler
CUDA / Stream Processors4,992
Memory Bandwidth480 GB/s
TDP300W
FP32 TFLOPS8.7

Current Offers

Used from £50
eBayUsed£50£4.17/GB

Prices last updated:

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Buying Guidance

Treat this as two old 12GB GPUs, not one 24GB accelerator. Even a very low price rarely compensates for Kepler compatibility, power draw and setup time.

Software: Kepler support has disappeared from many current CUDA and AI builds. Expect old drivers, old toolchains and missing modern kernels.

Price History

  • eBay£50mid-range
Aug 21Aug 28Sep 1Sep 4£50£50

For AI / LLM Use

Entry-level for local AI. Handles 7B-8B models well. 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?

  • 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 for 14B (tight fit)
  • Q6_K or Q8 for 7B models

Pros & Cons

Pros

  • Very low acquisition cost

Cons

  • 12GB usable VRAM: may need quantization for 30B+ models
  • 24GB is split across multiple GPUs; one model sees 12GB per GPU
  • Moderate memory bandwidth: not the fastest for inference
  • 300W TDP: high power draw
  • Older Kepler architecture: verify current CUDA support
  • No display output: headless only
  • May need aftermarket cooling solution
  • Only 12GB is usable by one model without explicit sharding
  • Kepler software support is obsolete for many current stacks
  • Dual-GPU 300W board with substantial cooling requirements

Community Verdict

  • r/LocalLLaMA

    Avoid. Dual-GPU means 12GB per die, Kepler lacks modern CUDA support, and it draws 300W. Buy a P40 instead.

    Source

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