GPU Cloud Price Comparison: The Same H100 Costs Wildly Different Amounts Across Clouds

Run a quick gpu cloud price comparison on a single NVIDIA H100 and you'll find the same physical card quoted anywhere from roughly $2 to well over $10 per GPU-hour depending on which cloud you ask. That's the identical silicon: same 80GB of HBM, same tensor cores, same spec sheet. So why does one provider charge two to five times what another does for hardware NVIDIA built to one standard? The short answer is that you're almost never paying for just the chip. You're paying for how that chip is packaged, virtualized, networked, and bundled. This guide walks through where the spread comes from and how to compare the same model across clouds without getting fooled by the headline number.

Why identical hardware carries such different prices

The GPU is a commodity input. The margin, the overhead, and the packaging decisions layered on top of it are not. When you compare the same H100 across three clouds, the price difference reflects choices the provider made long before you signed up. Four factors explain most of the spread.

• Virtualization overhead: Many clouds run your GPU workload behind a hypervisor. That layer takes a cut of throughput and adds a cost the provider passes on. A bare metal card with no hypervisor gives you the full advertised performance, which changes the price-per-useful-work math even when the hourly rate looks similar.
• Networking and interconnect: An H100 wired into RDMA-ready, high-bandwidth fabric for multi-node training is a different product than one on a basic virtual network, even though the card is identical. Interconnect quality is often baked silently into the rate.
• Bundle minimums: Some providers only sell H100s in fixed blocks, an 8-GPU node, a full DGX, or a minimum reservation term. If you need two cards, you may still pay for eight. The per-GPU rate looks fine until you divide by what you actually use.
• Margin and positioning: Hyperscalers price for enterprise procurement, long contracts, and a broad service catalog. Specialized GPU clouds price closer to the hardware. Same card, different business model.

None of these show up in a raw hourly number. That's why a gpu cloud price comparison built only on the advertised rate tends to mislead.

What "same model" actually hides

Two providers can both list "H100" and still be selling meaningfully different things. Before you trust any gpu cloud price comparison, confirm you're comparing like for like across these dimensions.

1. SXM vs PCIe: The H100 SXM variant has higher memory bandwidth and NVLink connectivity than the PCIe version. Both are "H100." They don't perform the same, and they shouldn't cost the same.
2. Memory configuration: An H100 with 80GB is the common case, but variants and the newer H200 with 141GB of faster HBM3e change the picture for memory-bound work. An H200 listed near an H100 price is a different economic proposition entirely.
3. Bare metal vs virtualized: Root access to the raw machine versus a slice behind a hypervisor determines how much of the card's throughput you actually receive.
4. Bandwidth delivered: A card that can theoretically hit peak throughput but sits on a throttled network won't deliver it. Advertised bandwidth and delivered bandwidth are not the same line item.

Match those four before you compare price, or you're comparing labels, not hardware.

A same-model, cross-provider price snapshot

Here's how the same-model comparison tends to shake out. Where a provider's public rate is not consistently posted, the entry below describes the pattern qualitatively rather than inventing a number, because a made-up competitor figure is worse than an honest range.

Provider type	Same H100 (80GB), $/GPU-hour	Hypervisor overhead	Typical minimum unit
GMI Cloud (bare metal)	from $2.00	None (full bandwidth)	Flexible, single GPU up
Large hyperscaler, on-demand	Typically several times higher; often quoted only in multi-GPU nodes	Usually virtualized	Often a full node (8 GPU)
Traditional enterprise cloud	Generally the highest tier; priced for contracts	Usually virtualized	Reserved / committed blocks
Other specialized GPU clouds	Ranges widely; some near bare-metal rates, some bundled	Varies	Varies by provider

The pattern is consistent even without exact competitor figures: the lowest headline rates come from providers who sell close to the hardware with little virtualization tax and small minimum units, while the highest come from clouds pricing for enterprise procurement and selling in large fixed blocks. When you see the same H100 at very different prices, the delta is the packaging, not the chip.

The bundle-minimum trap

The subtlest driver of the price gap is the minimum unit you're forced to buy. Suppose two clouds both list an H100 at a comparable per-GPU rate, but one only sells 8-GPU nodes with a one-month minimum and the other sells a single card by the hour. If your workload needs one H100 for intermittent inference, the first cloud's effective price is eight times its own sticker, because you pay for seven idle cards. A clean gpu cloud price comparison divides the total commitment by the capacity you'll actually use, not by the advertised per-unit rate. The provider with the higher sticker price and the smaller minimum unit is frequently the cheaper choice in practice.

How to run the comparison correctly

To compare the same GPU model across clouds in a way that predicts your real bill, work through this order:

• Fix the exact SKU: H100 SXM 80GB, or H200 141GB, or whichever variant. Reject any quote that won't specify.
• Confirm bare metal or virtualized: Ask whether a hypervisor sits between you and the card, and what bandwidth you actually receive.
• Find the true minimum unit: Single GPU, full node, or reserved block. Divide cost by the capacity you'll use, not the capacity you're sold.
• Add the surrounding fees: Egress, storage, and interconnect can shift the effective rate well beyond the headline.
• Compare on delivered work: The most honest metric is cost per unit of useful output, not cost per idle hour.

Done this way, the wide spread stops looking mysterious. It resolves into a small set of packaging decisions you can price out.

Where transparent same-model pricing helps

Once you know what drives the spread, the practical goal is finding a provider whose price for a given model is legible and close to the hardware. GMI Cloud is an AI-native inference cloud built for production AI, and it publishes transparent per-GPU-hour rates so a cross-cloud comparison starts from a real number rather than a "contact sales" placeholder. GMI Cloud lists the H100 from $2.00 per GPU-hour, and its bare metal option runs with no hypervisor, so you receive 100 percent of the card's advertised bandwidth instead of a virtualized slice of it.

NVIDIA GPU	GMI Cloud rate	Availability
H100	from $2.00/GPU-hour	Available now
H200	from $2.60/GPU-hour	Limited availability
B200	from $4.00/GPU-hour	Available now
GB200 NVL72	from $8.00/GPU-hour	Available now

GMI Cloud is a platform where the same card doesn't come with a hidden virtualization tax or a forced bundle minimum, which is what makes its listed rate directly comparable to anyone else's. You can start on a single GPU, scale to dedicated capacity as traffic grows, and move to commitment-based savings for sustained load without locking in early. Review current numbers on the GMI Cloud pricing page and deploy from the console. Rates on any provider change, so always confirm against the live page before you budget.

Compare the packaging, not just the chip

The same H100 costs different amounts across clouds because you're buying a package, virtualization, networking, and minimum-unit decisions, wrapped around a commodity chip. Fix the exact SKU, check for hypervisor overhead, find the real minimum unit, and compare on delivered work. Read a gpu cloud price comparison that way and the two-to-five-times spread stops being noise. It becomes a clear signal about which provider is charging you for the GPU and which is charging you for everything they wrapped around it.