GPU comparison

P100 vs RTX 5070

Specifications and current cloud pricing, side by side.

PascalvsBlackwellUpdated 32 days ago

The RTX 5070 wins for the most common use case of LLM inference because its 61.7 TFLOPS dense FP16 exceeds the P100 18.7 TFLOPS dense FP16 by more than threefold while sharing the same 250W TDP.

P100 listed from $0.09/GPU/hrRTX 5070 listed from $0.17/GPU/hr

Specifications Compared

SpecP100RTX 5070
TDP250W250W
VRAM16 GB12-16 GB
CUDA Cores3,5846,144
FP4 (dense)Not published493.9 TFLOPS
FP8 (dense)Not published123.5 TFLOPS
Memory TypeHBM2GDDR7
ArchitecturePascalBlackwell
FP16 (dense)18.7 TFLOPS61.7 TFLOPS
Form FactorsSXM2, PCIePCIe
INT8 (dense)Not published246.9 TOPS
InterconnectNVLink, PCIe 3.0PCIe 5.0
Tensor CoresNot published192
FP32 Performance9.3 TFLOPS30.9 TFLOPS
FP64 Performance4.7 TFLOPSNot published
Memory Bandwidth732 GB/s672 GB/s
FP4 (with sparsity)Not published987.8 TFLOPS
FP8 (with sparsity)Not published246.9 TFLOPS
FP16 (with sparsity)Not published123.5 TFLOPS
INT8 (with sparsity)Not published493.9 TOPS

Performance Analysis

Dense FP16 performance reaches 61.7 TFLOPS on the RTX 5070 compared with 18.7 TFLOPS dense FP16 on the P100 so training workloads that rely on dense FP16 arithmetic complete faster on the RTX 5070. Inference tasks that can apply sparsity see the RTX 5070 deliver 123.5 TFLOPS with sparsity against no published sparse figure for the P100. Memory bandwidth of 732 GB/s on the P100 permits modestly larger batch sizes than the 672 GB/s bandwidth on the RTX 5070 in bandwidth constrained kernels while the identical 250W TDP means power normalized throughput favors the RTX 5070 in dense FP16 workloads by the ratio of 61.7 to 18.7.

Current On-Demand Offers

Cheapest secure on-demand offer per provider that is reported in stock right now, per GPU per hour; multi-GPU instances show the whole-instance price alongside. Deploy opens the DeployGPU console for providers on the platform, otherwise the provider's own site. Stock is rechecked every minute.

P100

P100 is not offered on-demand by any provider we track right now. See the P100 rental page for last-seen listed prices and a price alert.

RTX 5070

RTX 5070 is not offered on-demand by any provider we track right now. See the RTX 5070 rental page for last-seen listed prices and a price alert.

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When to Choose the P100

The P100 suits scientific computing workloads that benefit from its 732 GB/s memory bandwidth and NVLink interconnect for multi GPU scaling. Legacy code bases compiled for Pascal architecture also run without modification on the P100 form factors that include SXM2.

When to Choose the RTX 5070

The RTX 5070 suits LLM training and inference that exploit its 61.7 TFLOPS dense FP16 and 30.9 TFLOPS FP32 figures together with PCIe 5.0 connectivity. Newer software stacks that utilize sparsity can further increase throughput to 123.5 TFLOPS with sparsity in FP16 operations.

Use Cases

LLM Training
RTX 5070

The RTX 5070 supplies 61.7 TFLOPS dense FP16 and 30.9 TFLOPS FP32 compared with 18.7 TFLOPS dense FP16 and 9.3 TFLOPS FP32 on the P100.

LLM Inference
RTX 5070

The RTX 5070 supplies 61.7 TFLOPS dense FP16 versus 18.7 TFLOPS dense FP16 on the P100 enabling higher throughput at the shared 250W TDP.

Fine-tuning
RTX 5070

The RTX 5070 supplies 61.7 TFLOPS dense FP16 and 123.5 TFLOPS with sparsity allowing faster iteration than the P100 18.7 TFLOPS dense FP16.

Stable Diffusion
RTX 5070

The RTX 5070 supplies 61.7 TFLOPS dense FP16 and 30.9 TFLOPS FP32 exceeding the P100 figures of 18.7 TFLOPS dense FP16 and 9.3 TFLOPS FP32.

Scientific Computing
P100

The P100 supplies 732 GB/s memory bandwidth and NVLink interconnect exceeding the RTX 5070 672 GB/s bandwidth and PCIe 5.0 interface.

Frequently Asked Questions

What is the FP32 performance difference between the P100 and RTX 5070?▾

The P100 delivers 9.3 TFLOPS FP32 while the RTX 5070 delivers 30.9 TFLOPS FP32 so the RTX 5070 provides more than three times the FP32 throughput at the same 250W TDP.

How does memory bandwidth compare on the P100 versus the RTX 5070?▾

The P100 provides 732 GB/s bandwidth with 16 GB HBM2 memory while the RTX 5070 provides 672 GB/s bandwidth with 12 to 16 GB GDDR7 memory.

Which GPU supports NVLink?▾

The P100 supports NVLink interconnect in addition to PCIe 3.0 while the RTX 5070 supports only PCIe 5.0 interconnect.

Do both GPUs share the same power limit?▾

Both the P100 and the RTX 5070 list a 250W TDP.

Which is cheaper to rent, the P100 or the RTX 5070?▾

Cloud rental prices for both the P100 and RTX 5070 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.

How much VRAM does the P100 have compared to the RTX 5070?▾

The P100 has 16 GB of HBM2 memory. The RTX 5070 has 12 to 16 GB of GDDR7 memory.

Can I find P100 and RTX 5070 GPUs available to rent right now?▾

Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.

What is the main difference between the P100 and the RTX 5070?▾

The P100 uses the Pascal architecture (2016) while the RTX 5070 uses Blackwell (2025). The RTX 5070 delivers 3.3x the dense FP16 throughput (61.7 vs 18.7 TFLOPS, both without sparsity) while the P100 has 1.1x the memory bandwidth of the RTX 5070.

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How this page is made

  • Specifications come from the NVIDIA, AMD and Intel datasheets for the P100 and the RTX 5070. Dense and sparse throughput are listed separately.
  • Prices and availability are live: every offer shown is an in-stock, on-demand listing from a provider we track, rechecked every minute.
  • The written comparison (overview, performance notes, when to choose each card, verdict, use cases and FAQ) was drafted with an AI model from the specification table above and passed an automated check that rejects any figure not in that table. It was last generated on . It contains no prices; those are always read live.
  • Read how we collect the data, or report an error on this page.

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