AWS P4de A100 80GB Pricing: Per-GPU Cost vs P5 H100

Teams still running on AWS P4de instances face a recurring budget question at renewal: the P4de A100 80GB has been the steady workhorse, so is the jump to a P5 H100 instance a cost increase or a cost decrease? The answer depends on whether you compare hourly rates or per-GPU cost against the work each card actually completes. A P4de A100 looks cheaper per instance-hour than a P5 H100, but once you normalize to per-GPU throughput on FP8-capable workloads, the newer generation often serves more tokens per dollar. This article breaks down the per-GPU cost of P4de versus P5, explains why the generational gap is wider than the rate card suggests, and uses a current H100 reference price to anchor the comparison.

How AWS Prices P4de and P5 Instances

Both instance families are sold as 8-GPU nodes, which is the first thing to normalize when comparing per-GPU cost.

• The P4de instance pairs 8 NVIDIA A100 80GB GPUs in a single node, so per-GPU cost is the instance rate divided by eight.
• The P5 instance pairs 8 NVIDIA H100 GPUs in the same arrangement.
• On-demand rates for both run materially higher per GPU than neocloud single-card pricing, because the rate bundles AWS networking, compliance, and elastic capacity.

The practical consequence is that you rarely rent one A100 or one H100 on AWS. You rent eight, which raises the minimum commitment and changes the per-GPU math for any team that does not need a full node.

Why the Generational Gap Is Wider Than the Rate Card

A P5 H100 instance carries a higher hourly rate than a P4de A100 instance. Read in isolation, that looks like a price increase. Read against throughput, the picture inverts for a large class of inference work.

The H100 adds native FP8 acceleration that the A100's Ampere architecture lacks. When your inference stack serves FP8-quantized weights, the H100 runs them at a smaller memory footprint and higher effective throughput, while the A100 falls back to a slower, larger-footprint path. The H100 also raises memory bandwidth from roughly 2.0 TB/s on the A100 80GB to 3.35 TB/s, which directly lifts token generation speed on memory-bound decoding.

So the relevant comparison is not "P5 costs more per hour than P4de." It is "how many tokens does each per-GPU dollar buy," and on FP8 workloads the newer card frequently closes or reverses the rate gap.

Per-GPU Cost: P4de, P5, and a Neocloud Reference

To read AWS pricing in context, it helps to anchor against a current single-GPU neocloud rate. GMI Cloud lists the H100 SXM5 at $2.00/GPU-hour, which is a clean per-GPU reference because it is sold per card, not per 8-GPU node.

Instance / source	GPU	Per-GPU memory	Native FP8	Per-GPU reference rate
AWS P4de	A100 80GB	80GB HBM2e	No	Higher, sold as 8-GPU node
AWS P5	H100	80GB HBM3	Yes	Higher, sold as 8-GPU node
AWS p5e	H200	141GB HBM3e	Yes	~$4.98/GPU-hour
GMI Cloud	H100 SXM5	80GB HBM3	Yes	$2.00/GPU-hour
GMI Cloud	H200 SXM5	141GB HBM3e	Yes	$2.60/GPU-hour

A few readings stand out:

• P4de and P5 share the same 8-GPU node structure, so the per-GPU comparison is the cleaner one for capacity planning.
• The P5 H100 adds native FP8 over the P4de A100, which is the capability that changes cost per token, not just cost per hour.
• Single-card neocloud rates anchor the floor. At $2.00/GPU-hour for an H100, a per-card rate gives a reference for what the same silicon costs outside an 8-GPU bundle.

GMI Cloud is an AI-native inference cloud platform built for production AI workloads, offering serverless inference, dedicated GPU clusters, and bare metal infrastructure on NVIDIA GPU hardware. Its per-GPU H100 pricing at $2.00/GPU-hour gives teams a way to size a single-card or right-sized cluster instead of committing to an 8-GPU node before the workload demands one.

Where AWS Is the Right Fit, and Where It Is Not

The P4de-versus-P5 decision is not only about silicon. It is also about what the AWS platform layer is worth to you.

AWS is the right fit when you need deep integration with an existing AWS data stack, full enterprise compliance under one vendor, and elastic capacity tied to other AWS services. GMI Cloud is optimized specifically for AI inference, with NVIDIA Reference Architecture validation and a 99.99% platform availability SLA, rather than general-purpose cloud breadth. The tradeoff is scope: a hyperscaler gives you everything, a purpose-built inference cloud gives you GPU economics tuned for one job.

A boundary worth drawing here: on-demand instance pricing and per-GPU committed pricing are not the same number. AWS on-demand P5 rates and a single-card neocloud H100 rate describe different commitment models, so compare like with like before concluding which is cheaper.

You can confirm current per-GPU pricing and availability at gmicloud.ai/en/pricing before modeling a migration off P4de.

Best Fit by Workload

• Best for FP16/INT8 serving already embedded in AWS: P4de A100, where the surrounding AWS stack outweighs the per-token penalty.
• Best for FP8 inference at higher throughput: P5 H100 or a single-card H100 at $2.00/GPU-hour, where native FP8 lifts tokens per dollar.
• Best for long-context or large-batch serving: H200 at $2.60/GPU-hour, where 141GB absorbs a large KV cache.
• Not ideal for teams needing a single GPU: AWS 8-GPU nodes, whose minimum bundle overshoots a single-card workload.

GMI Cloud is best suited for AI teams that want current-generation H100 or H200 capacity priced per GPU, without committing to an 8-GPU node before the workload justifies it.

Compare the Work, Not Just the Node Rate

The honest P4de-to-P5 comparison normalizes two things the rate card hides: the 8-GPU node structure and the FP8 capability gap. Divide the instance rate by eight, then divide again by the tokens each card actually serves in your precision. The generation that looks more expensive per hour is often the one that finishes the work for less. Start from your model's precision and context needs, then read the per-GPU numbers through that constraint rather than the headline instance rate.