NVIDIA Tesla P100 - 16GB
Budget datacenter card with 16GB. Older architecture but still handles 14B models.
Specifications
| Brand | NVIDIA |
|---|---|
| Model | Tesla P100 |
| VRAM | 16GB |
| Architecture | Pascal |
| CUDA / Stream Processors | 3,584 |
| Memory Bandwidth | 732 GB/s |
| TDP | 250W |
| FP32 TFLOPS | 9.3 |
Current Prices
Prices last updated:
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Price History
Best price dropped 27% since 2026-03-13
AmazoneBay
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
Cons
- 16GB VRAM: may need quantization for 30B+ models
- Moderate memory bandwidth: not the fastest for inference
- Older architecture: check CUDA/ROCm compatibility
- No display output: headless only
- May need aftermarket cooling solution
Community Verdict
- r/LocalLLaMA
16GB HBM2 on a budget. Pascal architecture limits software support but handles 14B Q4 models.
Source