H100 SXM vs A100 80GB: Which GPU Should You Choose?

We checked current provider rates and official specifications to settle when H100 SXM is worth choosing over A100 80GB.

Updated 2026-08-23 · refreshed daily

Choose H100 SXM for FP8 workloads, choose A100 80GB when hourly cost leads because H100 SXM carries a 2.9× on-demand price ratio, unless faster completion offsets the premium.

Before committing, check the live table to confirm that the on-demand minimums remain $1.74 for H100 SXM and $0.60 for A100 80GB.

Does your workload require native FP8 acceleration? Choose H100 SXM for FP8, BF16, or FP32 workloads within 80GB VRAM; choose A100 80GB only if BF16 or FP32 workloads within 80GB VRAM fully covers the workload.

Is finishing sooner worth the difference in total job cost? Choose H100 SXM when the shorter runtime matters; otherwise choose A100 80GB, since H100 SXM has a 0.91 effective cost-per-job ratio.

Can this workload safely restart after an interruption? Run a short interruptible A100 80GB rental to confirm the workload resumes cleanly from a checkpoint.

Today's prices

USD/hr · 1× GPU · lowest on-demand price per provider

Provider	H100 SXM	A100 80GB
Vast.ai	$1.74	$0.60
Hyperstack	$3.20 · Not reported	$1.35 · Not reported
DataCrunch	$3.25	$1.79
RunPod	$3.29	$1.39
Crusoe	$3.90 · Not reported	$2.00 · Not reported
Together AI	$3.99 · Not reported	Not offered
Paperspace	$5.95 · Not reported	Not offered
Jarvislabs	Not offered	$1.49 · Not reported

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2.90×

On-demand price ratio favoring A100 80GB unless H100 SXM earns back its premium through faster completion

0.91×

Near-parity on-demand cost per job makes turnaround time the deciding factor

$1270/mo

Monthly on-demand cost of keeping one H100 SXM continuously occupied

What your workload needs

Find your parameter count on the model's Hugging Face card. Serving maps to the inference rows, training to the fine-tuning rows.

Workload	VRAM needed	H100 SXM	A100 80GB	Cheapest today
70B Q4 inference	42 GB	1× $1.74/hr	1× $0.60/hr	A100 80GB ×1 → $0.6/hr
70B FP16 inference	168 GB	3× $1.74/hr	3× $0.60/hr	A100 80GB ×3 → $1.8/hr (est.)
70B QLoRA	46 GB	1× $1.74/hr	1× $0.60/hr	A100 80GB ×1 → $0.6/hr
7B full fine-tune	112 GB	2× $1.74/hr	2× $0.60/hr	A100 80GB ×2 → $1.2/hr (est.)
70B FP8 training	154 GB	2× $1.74/hr	unsupported	H100 SXM ×2 → $3.48/hr (est.)
Downshift recommendation	42 GB	1× $1.74/hr	1× $0.60/hr	A100 80GB ×1 → $0.6/hr

What the numbers say

H100 SXM carries an on-demand minimum of $1.74, while A100 80GB has an on-demand minimum of $0.60. That produces a 2.9× on-demand price ratio, so A100 80GB is the practical default when hourly budget matters more than faster completion.

H100 SXM has a 3.17× datasheet performance ratio over A100 80GB, not a measured workload speedup. Using that peak as an indirect runtime proxy yields a 0.91 on-demand effective cost-per-job ratio, so real job benchmarks should decide whether the faster completion offsets the hourly premium.

H100 SXM covers FP8, BF16, or FP32 workloads within 80GB VRAM, while A100 80GB shares the same memory ceiling but not every listed precision mode. If your workload requires the additional precision support, choose H100 SXM; otherwise, A100 80GB is the rational choice.

Before you rent

Data egress fees can raise the final bill; verify the provider's current per-GB transfer charge on its billing page.

A model that does not fit on one GPU forces a multi-GPU rental, multiplying billed GPU-hours for every run.

Interruptible rentals can stop mid-run, and infrequent checkpoints may turn discounted capacity into repeated compute.

Short experiments can cost more when usage rounds up to a billing increment; each provider's pricing page lists the minimum charge needed for an accurate estimate.

Marlin matches your workload requirements to the lowest-priced suitable GPU available across major cloud providers.

Coverage is limited to the listed providers and cited public sources, with freshness shown separately on the page. Prices and availability can change, while workload costs and performance comparisons rely on modeled assumptions and datasheet figures rather than measured results. https://www.tensordock.com/host-pricing · https://www.coreweave.com/pricing · https://vast.ai/pricing · https://www.paperspace.com/pricing · https://datacrunch.io/pricing · https://resources.nvidia.com/en-us-tensor-core/nvidia-tensor-core-gpu-datasheet · https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf · https://www.runpod.io/pricing · https://lambda.ai/service/gpu-cloud · https://www.hyperstack.cloud/gpu-pricing · https://www.together.ai/pricing · https://crusoe.ai/cloud/pricing · https://jarvislabs.ai/pricing

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