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On July 4, a harvester aggregated 1,417 GPU-rental price observations from Azure…

Brief

Compute pricing is highly non‑fungible: a single‑day harvest on July 4 collected 1,417 GPU rental quotes across Azure Retail Prices API, RunPod, Vast.ai and SemiAnalysis and showed H100 offers from $1.33 to $3.85/hr (median $2.59) and much wider spreads for other SKUs (e.g., H200 up to 8.8x, L4 13.9x). The author argues these gaps persist because quotes embed many non‑interchangeable attributes — term length, interconnect fabric, attached storage, region/legal constraints and counterparty SLA — so you cannot short one quote against another. By analogy to oil/gas/power (OTC → reference index → listed futures; WTI futures in 1983, Henry Hub 1990, Nord Pool 1996), compute is in the OTC phase and needs normalization at scale: pull every quote, decompose and price each attribute, and map bespoke offers onto standardized contracts. The post predicts an agentic normalization layer will precede and enable a true compute futures market.

Why it matters

On July 4, a harvester aggregated 1,417 GPU-rental price observations from Azure Retail Prices API, RunPod, Vast.ai and SemiAnalysis and normalized them to $/GPU-hour (one row per SKU).

Key details

  • H100 quotes (15 samples) ranged from $1.33 (Vast) to $3.85 (May on‑demand contract band) with a median of $2.59 — a 2.9x spread on the same day; other SKUs showed larger dispersion (RTXPRO6000: 27 quotes $0.33–$2.09, 6.3x; H200: 10 quotes $0.50–$4.39, 8.8x; L4 13.9x; V100 12x; B200 the tightest at 1.4x).
  • Price dispersion persists because quotes are heterogeneous and non‑fungible — differences in term (spot vs 1‑year commit), interconnect (SXM/NDR InfiniBand vs PCIe/Ethernet), included storage, region/legal constraints, and counterparty SLAs prevent simple arbitrage and there is no consolidated GPU tape.
  • Compute markets are at an OTC stage like early oil/gas/power markets; creating a fungible futures market requires large‑scale normalization (decompose every quote into SKU, quantity, term, fabric, storage, region, start window, counterparty, price each attribute) and an agentic layer that continuously maps bespoke supply into standardized contracts before an exchange can reliably clear.
Source evidence

Compute futures market will be the new oil

Itô (@itomarkets)

There is no single price of compute.

We run a harvester that aggregates GPU rental quotes across the venues we can reach programmatically: the Azure Retail Prices API, RunPod, Vast.ai, and the SemiAnalysis public contract preview. On July 4 it collected 1,417 price observations in one day. The chart shows every quote normalized to dollars per GPU-hour, one row per SKU.

What the data shows:

H100 is the row we care about most, so it's highlighted. 15 current quotes: $1.33 on Vast at the low, $1.99 to $3.29 on RunPod, $3.85 on the May on-demand contract band at the high. Median $2.59.

That's a 2.9x range between the cheapest and most expensive quote for the same chip on the same day. The difference comes from term (spot vs 1-year commit), interconnect, storage, and which venue you happen to be looking at.

The rest of the complex is wider.

RTXPRO6000: 27 quotes, $0.33 to $2.09, a 6.3x range.

H200: 10 quotes, $0.50 to $4.39, 8.8x, where the low end is a term commitment and the high end is spot from a venue with little competition on that SKU.

L4 runs 13.9x.

V100 runs 12x, mostly because clouds dumping legacy inventory and marketplaces renting scarce residual capacity price the same part very differently.

B200 is the tightest at 1.4x because few venues have it at all.

The reason this dispersion persists is simple: the quotes are heterogeneous, and the venues don't clear against each other. There is no consolidated tape for GPUs.

Normally a gap like that gets arbitraged: buy the cheap one, sell the expensive one, and the prices converge. That only works when the two things are interchangeable.

One barrel of WTI at Cushing is the same as another, one share of a stock is the same as another, and a central order book can match any buyer with any seller because of it.

Two H100 quotes at the same headline price can be completely different products.

One is spot, cancellable any hour; the other is a 1-year commit.

One is SXM on an NDR InfiniBand fabric that can run a distributed training job; the other is PCIe on Ethernet that cannot.

One includes 300TB of attached NFS; the other bills storage separately.

One is in a region your data can legally sit in; the other is not.

An Azure SLA and a marketplace host with one machine are also very different counterparties. The $1.33 Vast quote and the $3.85 contract band are different instruments once you read past the headline price, and you cannot short one against the other, which is why the gap stays open.

The same heterogeneity is why nobody has managed to build a compute exchange that works.

To run a central order book you have to standardize the contract: fix the SKU, the fabric, the term, the region, the delivery mechanism, the failure remedies. Every attribute you fix makes the contract cleaner to trade and cuts out more of the actual supply, because most real capacity doesn't match the standard spec.

If you keep the contract flexible instead, every trade needs bilateral negotiation again, and you have rebuilt OTC with extra steps.

Oil, gas, and power all went through this, and the second chart shows the sequence each of them followed. OTC comes first: bespoke physical deals negotiated one by one between principals, which is how oil traded for over a century.

Out of those bilateral prints a reference price emerges (Platts assessments for oil, Henry Hub for gas).

Only then can standardized futures list against the index, and once they do, the two layers settle into their permanent shape: OTC desks transfer the bespoke risk and hedge it on the exchange benchmark.

WTI futures listed in 1983, more than a century after oil started trading bilaterally.

Henry Hub futures listed in 1990. Nord Pool for power came in 1996.

Compute is at the OTC stage of that sequence right now, with the reference indices only just appearing and the futures beginning to list.

Getting from here to standardized contracts takes normalization at scale: pull every quote from every venue, decompose it into its attributes (SKU, quantity, price, term, fabric, storage, region, start window, counterparty), price each attribute, and map the bespoke thing a supplier is actually offering onto the standardized thing a contract can clear.

The distribution changes all day across dozens of venues and hundreds of SKU-region-term combinations.

A person can sample it a few times a day.

An agent can hold all of it continuously and re-normalize every time a quote moves. Our 1,417 observations came from one harvester on four venues in one day, and the full market is far larger.

That is why we think the agentic layer comes before the exchange rather than competing with it. The exchange needs a fungible underlying.

— https://nitter.net/itomarkets/status/2078263441307934735#m