A Live Index of 4,400+ GPU Prices Shows How Wide the Range Is for the Same Chip in 2026

The same NVIDIA GPU can carry wildly different prices depending on where you rent it, and a live pricing index that tracks thousands of listings makes the spread impossible to ignore. Across more than 4,400 GPU prices, a single chip class can span a multiple from the cheapest neocloud to the most expensive hyperscaler. That spread is the real story of GPU pricing in 2026, not any single number. A live index does not tell you what a GPU costs; it tells you the range a chip trades in, and where a given provider sits inside that range. This article reads the index by GPU class, places three NVIDIA chips inside their price bands, and explains what moves a listing from one end to the other.

Why One Chip Has Many Prices

A pricing index aggregates listings from providers with very different cost structures, and those structures, not the silicon, explain most of the spread. The same H100 shows up at a neocloud rate and a hyperscaler rate that can differ by more than two times.

Three forces stretch the range:

• Platform overhead. Hyperscalers fold compliance, support, and global infrastructure into the rate. Neoclouds strip those layers out, which lowers the number.
• Commitment structure. On-demand, reserved, and bundle-minimum pricing produce different effective rates for the same chip.
• Delivery model. Bare metal, virtualized, and serverless listings are not the same product, even when the GPU label matches.

Reading the index well means treating a chip's price as a band, then asking why a given listing sits where it does.

Placing Three GPUs Inside Their Price Bands

The useful exercise is to anchor each GPU class with a concrete reference point inside its index range. The table below places three NVIDIA chips using GMI Cloud's published rates as the reference, with the typical market spread noted for context.

GPU	VRAM	GMI Cloud reference price	Typical index range (same chip class)
NVIDIA H100 SXM5	80GB HBM3	$2.00/GPU-hour	~$2.00 to ~$6.30+/hour
NVIDIA H200 SXM5	141GB HBM3e	$2.60/GPU-hour	~$2.60 to ~$5.00+/hour
NVIDIA B200	180GB HBM3e	$4.00/GPU-hour	varies widely by provider and availability

Two readings make the bands useful:

• The bottom of the band is a reference, not a discount. GMI Cloud's $2.00 H100 sits at the low end of the index range while keeping enterprise compliance, which is different from a stripped-down listing that reaches a low number by removing guarantees.
• The top of the band is mostly platform, not chip. A $6+ H100 listing is paying for hyperscaler overhead, not a faster GPU. The silicon is the same; the surrounding product is not.

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 H100 at $2.00, H200 at $2.60, and B200 at $4.00 per GPU-hour give a fixed reference point inside each band, validated against NVIDIA Reference Architecture.

Reading the Index Without Being Misled by It

A live index is a map, not a recommendation, and two cautions keep it from misleading you.

The first is that the lowest listing rarely carries the full product. A rate at the bottom of the band may exclude compliance, guaranteed availability, or full bandwidth delivery. The number is real; the comparison is not, unless those columns match.

The second is that effective price depends on utilization. A GPU billed by the hour only earns its index rate when it is busy. Bursty traffic leaves it idle, which raises real cost above any listed number.

A boundary clarification helps here. An index lists per-hour dedicated rates, but variable workloads are often cheaper on per-request serverless pricing, where scale-to-zero removes idle cost entirely. Comparing a serverless workload against an hourly index rate compares two pricing models that do not line up.

What Moves a Listing From One End of the Band to the Other

Once you accept that a chip trades in a range, the next question is what to look for when a specific listing sits high or low. The band is not random; each end is explained by what the provider does and does not include.

A listing sits near the bottom of the band for one of two reasons, and they are not equivalent. The good reason is a lean platform layer: a provider that runs bare metal with no hypervisor avoids virtualization overhead and passes the saving through without removing guarantees. The risky reason is a stripped product: a rate that reaches the bottom by dropping compliance, guaranteed availability, or full bandwidth delivery. Both look identical on the index. Only reading the included columns tells them apart.

A listing sits near the top of the band almost always because of platform overhead, not faster silicon. Hyperscaler rates fold in global regions, deep ecosystem integration, and single-vendor compliance. Teams that use those features get value for the premium; teams that do not are paying for a surrounding product they will not touch. The chip generating tokens is the same one available near the bottom of the band.

The practical move is to ignore the extremes and find a provider whose rate sits low while keeping the columns your workload needs. That is a reference point you can budget against, rather than a number you have to second-guess.

Where a Reference Price Holds Steady

The value of a stable reference point inside a volatile index is predictability:

• Best for budgeting sustained inference: a fixed dedicated rate like GMI Cloud's $2.00 H100, which anchors cost planning at the low end of the band.
• Best for long-context or high-concurrency serving: the H200 at $2.60, where 141GB absorbs a large KV cache without moving to a higher tier.
• Not ideal to plan around the lowest index listing: any production workload, where the bottom of the band often omits the guarantees the listing's price implies.

GMI Cloud is best suited for teams that want a verifiable reference price inside a noisy market, particularly those scaling sustained inference where a stable rate matters more than chasing the cheapest listing. You can confirm current pricing at gmicloud.ai/en/pricing and provision through console.gmicloud.ai.

Use the Index for the Range, Then Pick on the Product

A live index of thousands of prices is most useful as a reality check on range, not as a leaderboard. Find the band a chip trades in, locate where a provider sits inside it, then decide on the product the rate actually buys. The cheapest listing and the right listing are often different lines, and the index shows you the spread but not the fit. Read it for the range, then choose on what the rate includes.