GPU comparison

H100 vs RTX 3080

Specifications and current cloud pricing, side by side.

HoppervsAmpereUpdated 34 days ago

The H100 wins for the most common use case of LLM training because 989 TFLOPS dense FP16 and 80 to 94 GB memory exceed the corresponding 59.5 TFLOPS and 10 to 12 GB figures on the RTX 3080.

H100 listed from $2.59/GPU/hrRTX 3080 listed from $0.13/GPU/hr

Right now, from live stock

  • Cheapest right now: H100 at $2.59/hr on QuantaCloud

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  • Most providers in stock: H100 (9)

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Specifications Compared

SpecH100RTX 3080
TDP700W320W
VRAM80-94 GB10-12 GB
CUDA Cores16,8968,704
FP8 (dense)1,979 TFLOPSNot published
Memory TypeHBM3GDDR6X
ArchitectureHopperAmpere
FP16 (dense)989 TFLOPS59.5 TFLOPS
Form FactorsSXM5, PCIe, NVLPCIe
INT8 (dense)1,979 TOPS238 TOPS
InterconnectNVLink, PCIe 5.0, InfiniBandPCIe 4.0
Tensor Cores528272
FP32 Performance67 TFLOPS29.8 TFLOPS
FP64 Performance34 TFLOPSNot published
Memory Bandwidth3,350 GB/s760 GB/s
FP8 (with sparsity)3,958 TFLOPSNot published
FP16 (with sparsity)1,979 TFLOPS119 TFLOPS
INT8 (with sparsity)3,958 TOPS476 TOPS

Performance Analysis

The H100 reaches 989 TFLOPS in dense FP16 while the RTX 3080 reaches 59.5 TFLOPS in dense FP16. The H100 reaches 67 TFLOPS in FP32 while the RTX 3080 reaches 29.8 TFLOPS in FP32. Training workloads that use dense FP16 benefit from the higher 989 TFLOPS figure on the H100 because larger batch sizes become feasible with 3350 GB per second bandwidth. Inference workloads that use dense FP16 see similar scaling because 989 TFLOPS exceeds 59.5 TFLOPS by a wide margin. The H100 also supplies 1979 TFLOPS with sparsity in FP16 compared to 119 TFLOPS with sparsity on the RTX 3080.

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
QuantaCloudus-midwest-21$2.59—Deploy
HyperstackCANADA-18$2.60$20.80Deploy
Massed Computeus-central-32$2.73$5.46Deploy
Oridallas-28$2.90$23.20Deploy
ScalewayParis, France (PAR2)2$3.72$7.45Deploy

9 providers in stock, 19 offers (cheapest per provider shown). All H100 offers, price history and alerts

RTX 3080

RTX 3080 is not offered on-demand by any provider we track right now. See the RTX 3080 rental page for last-seen listed prices and a price alert.

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When to Choose the H100

The H100 suits large scale training jobs that require 80 to 94 GB of memory and 3350 GB per second bandwidth. Workloads that need 989 TFLOPS dense FP16 or 1979 TFLOPS with sparsity in FP16 run more efficiently on the H100 than on GPUs limited to 59.5 TFLOPS dense FP16.

When to Choose the RTX 3080

The RTX 3080 suits smaller inference tasks that fit in 10 to 12 GB of memory and run at 29.8 TFLOPS in FP32. Power constrained environments benefit from the 320 watt TDP when dense FP16 performance at 59.5 TFLOPS meets requirements.

Use Cases

LLM Training
H100

The H100 supplies 989 TFLOPS dense FP16 and 3350 GB per second bandwidth that support larger models than the 59.5 TFLOPS and 760 GB per second on the RTX 3080.

LLM Inference
H100

The H100 supplies 1979 TFLOPS with sparsity in FP16 that exceed the 119 TFLOPS with sparsity on the RTX 3080.

Fine-tuning
H100

The H100 supplies 1979 TOPS dense INT8 that exceed the 238 TOPS dense INT8 on the RTX 3080.

Stable Diffusion
RTX 3080

The RTX 3080 supplies sufficient 29.8 TFLOPS in FP32 within a 320 watt TDP for workloads that fit in 10 to 12 GB memory.

Scientific Computing
H100

The H100 supplies 67 TFLOPS in FP32 and 3350 GB per second bandwidth that exceed the 29.8 TFLOPS and 760 GB per second on the RTX 3080.

Frequently Asked Questions

What is the memory bandwidth difference between the H100 and RTX 3080?▾

The H100 reaches 3350 GB per second. The RTX 3080 reaches 760 GB per second.

How do the FP16 dense figures compare on the H100 and RTX 3080?▾

The H100 reaches 989 TFLOPS in dense FP16. The RTX 3080 reaches 59.5 TFLOPS in dense FP16.

What TDP values apply to the H100 and RTX 3080?▾

The H100 carries a 700 watt TDP. The RTX 3080 carries a 320 watt TDP.

Which GPU offers higher FP32 performance?▾

The H100 reaches 67 TFLOPS in FP32. The RTX 3080 reaches 29.8 TFLOPS in FP32.

How does INT8 dense performance differ between the two GPUs?▾

The H100 reaches 1979 TOPS in dense INT8. The RTX 3080 reaches 238 TOPS in dense INT8.

Which is cheaper to rent, the H100 or the RTX 3080?▾

Cloud rental prices for both the H100 and RTX 3080 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 3080?▾

The H100 has 80 to 94 GB of HBM3 memory. The RTX 3080 has 10 to 12 GB of GDDR6X memory.

Can I find H100 and RTX 3080 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 3080?▾

The H100 uses the Hopper architecture (2022) while the RTX 3080 uses Ampere (2020). The H100 delivers 16.6x the dense FP16 throughput (989 vs 59.5 TFLOPS, both without sparsity) and 4.4x the memory bandwidth of the RTX 3080.

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How this page is made

  • Specifications come from the NVIDIA, AMD and Intel datasheets for the H100 and the RTX 3080. 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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