The NVIDIA Tesla P100 was NVIDIA’s first HBM2 data-center GPU. It is now dated, but its 16 GB of fast memory and strong FP64 performance keep it useful for scientific computing, classic deep learning and budget AI inference.
In this 2026 guide we cover the Tesla P100’s specs, what it can (and cannot) do today, where you can still rent it, and which GPU to choose instead if you need more power for AI.
Table of Contents
Tesla P100 Specifications
| Spec | Tesla P100 |
|---|---|
| Architecture | Pascal (GP100), 2016 |
| CUDA cores | 3,584 |
| Memory | 16 GB HBM2 |
| Memory bandwidth | 732 GB/s |
| FP64 / FP32 / FP16 | ~4.7 / 9.3 / 18.7 TFLOPS (PCIe) |
| Tensor cores | None |
Is the Tesla P100 Still Worth Renting in 2026?
For FP64 scientific workloads and learning, yes. For modern AI it is limited: no Tensor cores, and NVIDIA has moved Pascal to maintenance-only drivers, so newest framework features may not support it.
It is also the GPU behind Kaggle’s free notebooks, so many people learn deep learning on it.
What it is good for
- FP64 scientific computing and simulations
- Classic deep learning (TensorFlow, PyTorch older versions)
- Budget inference of small models
- Learning CUDA
AI workloads on the Tesla P100
14B LLMs in 8-bit, 24B LLMs in 4-bit (Devstral Small 2), SDXL and Flux in FP8, and fine-tuning small models with LoRA.
Best Tesla P100 Hosting Providers
1. GPU Mart
- Dedicated GPU servers and GPU VPS in US data centers
- Professional Dedicated P100 (16 GB), 16-core dual Xeon, 128 GB RAM
- Windows or Linux with full admin access
- Tesla P100 $159/mo
Pros
- Very low monthly prices
- Dedicated hardware, no noisy neighbours
- One-click AI apps (Ollama, Stable Diffusion)
Cons
- Monthly billing only
- US locations only
GPU Mart offers a dedicated Tesla P100 for $159/month with 128 GB of RAM.
2. Kaggle
- Free Jupyter notebooks with GPU
- About 30 GPU hours per week
- P100 or 2× T4
- Free: about 30 GPU hours per week (T4 or P100)
Pros
- Completely free
- Great for learning
- Datasets and competitions built in
Cons
- Session time limits
- Not for production
Kaggle notebooks offer a free P100 for roughly 30 hours per week, perfect for learning.
3. Vast.ai
- Marketplace with 68+ GPU types
- Per-second billing, interruptible discounts
- Docker templates for AI and gaming workloads
- Market pricing: RTX 4090 from ~$0.30/hr · A100 80GB from ~$0.43/hr · interruptible 50%+ cheaper
Pros
- Often the cheapest hourly prices
- Huge choice of consumer GPUs
- No long-term commitment
Cons
- Quality varies by host
- Availability of older cards changes daily
Some Vast.ai hosts list P100s at very low hourly prices.
Tesla P100 vs Alternatives (Monthly Prices)
If your workload needs more speed or VRAM, these are the closest upgrades available as hosted servers today:
| GPU | VRAM | From | Notes |
|---|---|---|---|
| Tesla V100 | 16 GB | $119.60/mo | Tensor cores, much faster AI |
| RTX A4000 | 16 GB | $119–$209/mo | Modern Ampere, FP16/BF16 |
| A100 40GB | 40 GB | $639/mo | Modern data-center GPU |
How to Choose
- Match VRAM to your AI model. 8 GB for 7–8B LLMs, 16 GB for 14–24B, 24 GB for 27–32B, 48 GB for 70B in 4-bit.
- Hourly vs monthly. For tests and short jobs use RunPod or Vast.ai. For 24/7 use, compare the monthly total (hourly price × 730 hours) with a fixed-price monthly server from GPU Mart or HOSTKEY, which includes the whole machine, storage and bandwidth.
- Dedicated vs shared. Dedicated servers give stable performance; marketplace GPUs are cheaper but vary by host.
- Location. Pick EU hosting (HOSTKEY, OVHcloud) for GDPR-sensitive data.
FAQ
GPU Mart offers a dedicated P100 server for $159/month. Kaggle offers free P100 notebooks with weekly limits.
Only for small models and learning. It lacks Tensor cores; a V100 or RTX A4000 is a better AI choice at a similar price.
Conclusion
The P100 is now a budget HPC and learning GPU. Use Kaggle for free experiments or GPU Mart for a $159/month server; for AI, the V100 at $119.60/month is the smarter buy.
Prices were checked in September 2026 and change often. Always confirm current pricing on the provider’s website before you order.