GPUDojo

NVIDIA Tesla P100 - 16GB usable

Fast HBM2 bandwidth on a budget, but 16GB capacity and Pascal-era support make it a specialist choice.

Specifications

BrandNVIDIA
ModelTesla P100
Usable VRAM16GB
ArchitecturePascal
CUDA / Stream Processors3,584
Memory Bandwidth732 GB/s
TDP250W
FP32 TFLOPS9.3

Current Offers

No fresh used offer today · recently £229 on eBay

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Buying Guidance

Choose it when bandwidth matters more than fitting a 24GB-class model. Confirm PCIe form factor, cooling, power connection and framework compatibility before purchase.

Software: The P100 has strong HBM2 bandwidth and native FP16, but its Pascal generation misses features expected by some newer inference stacks.

Lowest tracked offers by retailer

UK · GBP · Weekly since Aug 2026 · Shipping may be extra

  • eBay (no current offer)
£100£125£150£175£200£225£250Aug 21Aug 28Sep 4Sep 11Sep 18eBay £229
About these prices

Each line is the lowest offer we tracked from that retailer at weekly snapshots, ending at today's offer. The condition can vary between snapshots (new, used, or unrecorded for older ones). eBay prices exclude shipping.

Weekly offers table
Lowest tracked offer per retailer · UK · GBP
DateeBay
Sep 18, 2026 £228.88
Sep 11, 2026 £118.00
Sep 4, 2026 £118.00
Sep 1, 2026 £118.00
Aug 28, 2026 £118.00
Aug 21, 2026 £119.26

For AI / LLM Use

Good for 14B models. 30B requires aggressive quantization. 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 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: ~55 tokens/sec

Prefill: ~166 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

  • High-bandwidth HBM2 memory for its used price
  • Often faster than a P40 when the workload fits in 16GB

Cons

  • 16GB usable VRAM: may need quantization for 30B+ models
  • Moderate memory bandwidth: not the fastest for inference
  • Older Pascal architecture: verify current CUDA support
  • No display output: headless only
  • May need aftermarket cooling solution
  • Only 16GB, so many 30B-class configurations will not fit
  • Passive cooling and no display output

Community Verdict

  • r/LocalLLaMA

    16GB HBM2 on a budget. Pascal architecture limits software support but handles 14B Q4 models.

    Source

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