GPU comparison

H100 vs RTX 5000 Ada

Specifications and current cloud pricing, side by side.

HoppervsAda LovelaceUpdated 33 days ago

The H100 wins for the most common use case of LLM training because its 80 to 94 GB HBM3 memory and 3350 GB/s bandwidth exceed the RTX 5000 Ada specifications by wide margins while delivering 989 TFLOPS FP16 dense performance.

H100 listed from $2.50/GPU/hrRTX 5000 Ada listed from $0.55/GPU/hr

Right now, from live stock

Specifications Compared

SpecH100RTX 5000 Ada
TDP700W250W
VRAM80-94 GB32 GB
CUDA Cores16,89612,800
FP8 (dense)1,979 TFLOPSNot published
Memory TypeHBM3GDDR6
ArchitectureHopperAda Lovelace
FP16 (dense)989 TFLOPSNot published
Form FactorsSXM5, PCIe, NVLPCIe
INT8 (dense)1,979 TOPSNot published
InterconnectNVLink, PCIe 5.0, InfiniBandPCIe 4.0
Tensor Cores528400
FP32 Performance67 TFLOPS65.3 TFLOPS
FP64 Performance34 TFLOPSNot published
Memory Bandwidth3,350 GB/s576 GB/s
FP8 (with sparsity)3,958 TFLOPS1,044.4 TFLOPS
FP16 (with sparsity)1,979 TFLOPSNot published
INT8 (with sparsity)3,958 TOPSNot published

Performance Analysis

The FP16 dense performance reaches 989 TFLOPS on the H100 while the corresponding figure remains unpublished for the RTX 5000 Ada. This gap affects training workloads that rely on dense FP16 operations. The H100 also lists FP8 dense performance at 1979 TFLOPS against the RTX 5000 Ada FP8 figure of 1044.4 TFLOPS with sparsity. Memory bandwidth of 3350 GB/s on the H100 versus 576 GB/s on the RTX 5000 Ada directly influences maximum batch sizes during both training and inference. The FP32 rating of 67 TFLOPS on the H100 sits close to the 65.3 TFLOPS on the RTX 5000 Ada yet pairs with far higher memory capacity and bandwidth on the H100.

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.

H100

ProviderRegionGPUsPer GPU / hrInstance / hrDeploy
HyperstackCANADA-12$2.50$5.00Deploy
QuantaCloudus-midwest-22$2.59$5.18Deploy
Massed Computeus-central-31$2.73—Deploy
Oridallas-28$2.90$23.20Deploy
RunPodglobal1$3.19—Deploy

8 providers in stock, 20 offers (cheapest per provider shown). All H100 offers, price history and alerts

RTX 5000 Ada

RTX 5000 Ada is not offered on-demand by any provider we track right now. See the RTX 5000 Ada rental page for last-seen listed prices and a price alert.

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When to Choose the H100

The H100 suits workloads that require 80 to 94 GB of HBM3 memory and 3350 GB/s bandwidth. Its FP16 dense rating of 989 TFLOPS and FP8 dense rating of 1979 TFLOPS support large model training and inference where the 700W TDP remains acceptable.

When to Choose the RTX 5000 Ada

The RTX 5000 Ada fits deployments limited to 250W TDP and PCIe form factor. Its 32 GB GDDR6 memory and 576 GB/s bandwidth handle moderate inference or fine tuning tasks that do not exceed the published FP8 with sparsity rating of 1044.4 TFLOPS.

Use Cases

LLM Training
H100

The H100 supplies 80 to 94 GB HBM3 memory and 3350 GB/s bandwidth that exceed the 32 GB and 576 GB/s on the RTX 5000 Ada.

LLM Inference
H100

FP16 dense performance of 989 TFLOPS on the H100 enables larger batch inference than the unpublished FP16 rating on the RTX 5000 Ada.

Fine-tuning
H100

The H100 FP8 dense rating of 1979 TFLOPS combined with higher memory capacity supports fine tuning of models that exceed 32 GB.

Stable Diffusion
RTX 5000 Ada

The RTX 5000 Ada TDP of 250W and PCIe form factor suffice for diffusion workloads that fit within 32 GB GDDR6 memory.

Scientific Computing
H100

Frequently Asked Questions

What memory configurations distinguish the two GPUs?▾

The H100 provides 80 to 94 GB HBM3 while the RTX 5000 Ada contains 32 GB GDDR6. Bandwidth reaches 3350 GB/s on the H100 versus 576 GB/s on the RTX 5000 Ada.

How do the TDP values compare?▾

The H100 lists a TDP of 700W. The RTX 5000 Ada lists a TDP of 250W.

Which GPU publishes an FP16 dense figure?▾

The H100 publishes an FP16 dense figure of 989 TFLOPS. The RTX 5000 Ada does not publish an FP16 figure.

How does FP8 performance differ?▾

The H100 lists FP8 dense performance of 1979 TFLOPS. The RTX 5000 Ada lists FP8 performance of 1044.4 TFLOPS with sparsity.

What interconnect options exist?▾

The H100 supports NVLink, PCIe 5.0, and InfiniBand. The RTX 5000 Ada supports PCIe 4.0.

Which is cheaper to rent, the H100 or the RTX 5000 Ada?▾

Cloud rental prices for both the H100 and RTX 5000 Ada 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 H100 have compared to the RTX 5000 Ada?▾

The H100 has 80 to 94 GB of HBM3 memory. The RTX 5000 Ada has 32 GB of GDDR6 memory.

Can I find H100 and RTX 5000 Ada 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 H100 and the RTX 5000 Ada?▾

The H100 uses the Hopper architecture (2022) while the RTX 5000 Ada uses Ada Lovelace (2023). The H100 has 80-94 GB of HBM3 at 3,350 GB/s; the RTX 5000 Ada has 32 GB of GDDR6 at 576 GB/s, so the H100 has 5.8x the memory bandwidth of the RTX 5000 Ada.

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How this page is made

  • Specifications come from the NVIDIA, AMD and Intel datasheets for the H100 and the RTX 5000 Ada. 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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