Nvidia P100

Nvidia P100

Legacy Pascal data center GPU for budget scientific computing.

Compare vs other GPUs →
Aggregating historical prices...

Weekly median price per GPU per hour.

At a glance

Hardware

Architecture
Pascal
Memory per GPU
16 GB HBM2
Memory bandwidth
732 GB/s
Release date
Q2 2016

Market Updated 4 minutes ago

Cheapest in stock
$0.09 / GPU / hr
on-demand · 4x · Vast.ai
Median price
$1.42 / GPU / hr
on-demand
90-day trend
−7%
median on-demand
Coverage
6 providers
13 of 67 configs in stock

P100 Pricing and Availability

How this list works

The two views

By provider shows one card per company, placed where its best offer ranked. By configuration lists every offer.

Order

What you can rent comes first: in stock, then waitlist, then not reported, then out of stock. Priced offers rank ahead of quote-only, and on-demand ahead of other billing types.

Within a group, five factors set the order:

  1. Location: datacenter proximity, blended with provider HQ.
  2. Price: hourly price, per GPU and in total.
  3. Billing type: reserved ahead of spot, spot ahead of quote-only.
  4. Specs: more VRAM, vCPUs and RAM.
  5. Provider diversity: a provider's repeat rows rank slightly lower, so one company can't take all the top spots.

Search

We match the provider, its country, GPU model, form factor, billing type, availability and instance name. Matching is partial and case-insensitive. Everyday words work too, so "interruptible" finds spot and "sold out" finds out of stock.

Transparency and funding

  • Ads and sponsors: Paid placements sit at the top of the list and are always labeled as sponsored content. Sponsorship never influences the organic ranking itself.
  • Affiliates: Affiliate links are marked. We may earn a commission if you click them, but commissions never affect the order.
  • Prices: Shown in USD, converted at daily reference rates where a provider publishes in another currency. A month means 720 hours. How we estimate costs.
Vast.ai logo

Vast.ai

Our sponsor

United States of America flag USA 12 configs 1x-4x

On-Demand from $0.09 Spot from $0.08
From $0.09 / GPU / hr On-Demand Visit website
Scaleway logo

Scaleway

In stock

France flag France 1 config 1x

On-Demand from $1.42
From $1.42 / GPU / hr On-Demand Visit website
Oracle Cloud logo

United States of America flag USA 2 configs 1x-2x

On-Demand from $1.27
From $1.27 / GPU / hr On-Demand Visit website
Google Cloud logo

United States of America flag USA 36 configs 1x-4x

On-Demand from $1.51 Reserved from $0.80 Spot from $0.77
From $1.51 / GPU / hr On-Demand Visit website
Alibaba Cloud logo

Singapore flag Singapore 12 configs 1x-8x

On-Demand from $1.55 Reserved from $0.87
From $1.55 / GPU / hr On-Demand Visit website
Database Mart logo

United States of America flag USA 4 configs 1x

Reserved from $0.12
From $0.12 / GPU / hr Reserved (24mo) Visit website

Heads up: A provider's own page may quote a different figure, for example on tax, region or a promotion. A monthly-only plan shows a derived hourly rate. Verify before provisioning. More on how we price.

What size AI models can the P100 run?

One P100 has 16 GB of VRAM. In practice, that's enough memory for roughly 8B parameters at 4-bit or 2B at 16-bit, assuming a 32K context. Below are some open-weight LLMs, with the estimated memory and GPUs each one needs.

Single node ≤ 8 Multi-node
Model Memory (INT4 / FP4) P100s needed Cost / hr Cost / mo
Alibaba Cloud logo Qwen3.8-27B 27B
19 GB
2
$2.84
$2,045
Google Cloud logo Gemma 4 31B 30.7B
24 GB
2
$2.84
$2,045
OpenAI logo GPT-OSS-120B 117B · 5.1B active
68 GB
5
DeepSeek logo DeepSeek V4 Flash 284B · 13B active
161 GB
12
Z.AI logo GLM-5.3-Flash 320B · 18B active
178 GB
13
MiniMax logo MiniMax-M3 428B · 23B active
241 GB
17
Moonshot AI logo Kimi K3 2.8T · 104B active
1,545 GB
108

Estimates based on the median on-demand rate. Memory is weights plus FP16/BF16 KV cache at 32K context per request. GPU counts assume 90% of advertised VRAM is usable. No guarantee of runtime support, usable performance, or that a matching quantized build exists. How we estimate costs.

Technical Specifications

Nvidia P100 · Per GPU

Compute · dense
FP16 / BF16 18.7 TFLOPS
FP32 9.3 TFLOPS
FP64 4.7 TFLOPS
Precision support FP16FP32FP64
Memory
Capacity 16 GB HBM2
Bandwidth 732 GB/s
Bus width 4,096-bit
ECC Yes
Silicon
Architecture Pascal
Process TSMC 16nm FinFET
Transistors 15.3 billion
Shader cores 3,584 CUDA cores
Compute units 56 SMs
Fabric and host
GPU interconnect NVLink 160 GB/s
Host interface PCIe 3.0
Power
Board power 250 W
Cooling Passive

Source: official Nvidia P100 datasheet.

Frequently Asked Questions

How much does the P100 cost per hour?

As of September 11, 2026, the median on-demand price is $1.42 per GPU per hour across 5 providers.

Billing typeConfigsMedian / GPU / hrCheapest

On-demand

25

$1.42

$0.09 (in stock, Vast.ai)

Reserved

28

$1.04

$0.12 (24 mo)

Spot

14

$0.08 (in stock, Vast.ai)

No median for spot: we only show this when at least 3 providers list the GPU on that billing type.

Who has the cheapest P100?

As of September 11, 2026, the cheapest verified in-stock on-demand P100 tracked by GetDeploying is $0.09 per GPU per hour from Vast.ai. Cheapest verified in stock on other billing types: spot $0.08, also from Vast.ai.

How much does the P100 cost per month?

As of September 11, 2026, at 720 hours per month, one P100 costs an estimated $1,022 at the median on-demand price. Cheapest verified in stock: $65 per month on-demand, $58 spot.

Where can I rent a P100?

GetDeploying currently tracks P100 configs from 6 providers. Vast.ai and Scaleway have verified in-stock on-demand configs. See the full price comparison above for every provider and config.

Are P100 prices going up or down?

As of September 11, 2026, the median on-demand price has fallen about 7% over the past 90 days to $1.42 per GPU per hour, though it is about 11% above where it was a year ago.

How many P100 GPUs do I need?

One P100 runs models up to roughly 8B parameters at 4-bit quantization or 2B at 16-bit, assuming a 32K context. Larger models run across multiple GPUs: the model table above shows the estimated memory and GPU count for popular open-weight LLMs.

Why choose the P100?

16GB HBM2 with FP16 and FP64 support. Very low cost per hour. Useful for legacy workloads, scientific computing, and basic GPU development.

When is the P100 not a good fit?

Pascal architecture, 3+ generations behind. No Tensor Cores, no BF16, no INT8. Only viable for legacy FP32/FP64 scientific workloads or basic GPU development.

Alternatives to Nvidia P100

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