The secondary market for GPU compute

Compute you already paid for,
now tradable.

Sell the GPU capacity you’re not using. Get GPUs for a month, set up and verified.

01

Bought in years.

A GPU contract is one solid block: every node, every month, paid for up front.

02

Capacity sits idle.

The capacity a team isn’t using sits under the line. Already paid for, and today there’s nowhere to sell it.

03

Now it moves.

We move that window, node by node, to a buyer who needs it. With the data centre’s consent, set up to spec, and verified before any money moves.

Reference 2026-11-02
H100$3.27-1.6%
H200$5.04+0.5%
B200$8.19+0.3%
B300$9.84+0.3%
The problem

GPUs are sold like a building and used like electricity.

You sign for three to five years and pay for all of it, every month. You only need that much in your busiest month.

What you pay for, every month
What you actually useMicrosoft’s own clusters averaged 52% utilisation · Philly trace
  1. 1
    The gap is paid for, and it perishes.

    An unused GPU-month cannot be stored.

  2. 2
    Smaller buyers are locked out.

    A startup cannot sign three years. Same H100:

    $2.70
    on contract
    $3.32
    neocloud hourly
    $12.29
    Azure
  3. 3
    And the gap cannot be sold.

    The contract forbids it. The machines are set up for whoever holds them.

How it works

Sellers move the capacity they are not using. Buyers pay under the hourly rate.

The data centre agrees once. The seller lists a window on a link. The buyer takes it at the posted price, delivered and verified. The contract never moves. See a request link

How one trade works25%
1Buys
A company buys three years of GPUs.

512 of them, paid up front.

The seller’s fleet · 512 GPUs, three yearspaid up front, whether it runs or not
Running
Running
5120
Year 1Oct 2026 → Mar 2027Year 3
RunningSpare, different every month
The seller gets
$0.00M
cash, not credits that expire
The buyer gets
3 months
with no three-year commitment
The contract moves
Nowhere
we keep $0.40/hr in between, $224K on this trade
The premium

Compute is bought in years and used in months.

The short window is the scarce thing, so it carries the price. That gap is what a holder can sell and a buyer can still save on.

What one GPU-hour costs, by how you buy it
36-month contractCivo$3.79
On demand, neocloudmedian of 21 providers$6.49
On demand, hyperscalerAWS$14.24
+71%on demand over the contract3.8×hyperscaler over the contractgetdeploying.com, 27 Sep 2026
The seller

Cash for months that would bill for nothing.

The buyer

Under the hourly rate, with no three-year commitment.

The desk

The spread on a matched trade. The buyer signs first; we never hold capacity.

Not free money. The premium pays for carrying the months and delivering the block.

Delivery

How a GPU actually moves.

A block is not a login. The machines are rebuilt for the next tenant, measured against the spec, and accepted before the seller is paid.

  1. 01Consent
    0 to 2 days

    The data centre agreed once. Each sub-tenant is confirmed against it.

  2. 02Screening
    3 to 10 days

    KYB, ownership, sanctions and export checks.

  3. 03Partition and wipe
    2 to 3 days

    Fabric partitioned, disks wiped, firmware pinned to the listing.

  4. 04Attestation
    hours

    Serials, network and storage measured against the listing.

  5. 05Acceptance
    48 hours

    The buyer tests in-tenancy. If it fails, it walks.

  6. 06Record
    on release

    Hashed and timestamped. Funds release. The trade prints.

What is attested before acceptance
  • IdentitySerials, firmware, driver, node count and topology, as listed
  • FabricAll-reduce of 380 GB/s or better across 32 nodes, one island
  • Storage2 GB/s per GPU, sustained
  • HealthThe operator's node events plus the buyer's own probes
What counts as down, and what happens
  • Xid classes 13, 31, 48, 79
  • ECC errors over threshold
  • NCCL bus bandwidth below the floor
  • Thermal throttle
1 in 16
hot spare nodes, per lot
45 min
node replacement clock

Tested on two data centres. Nothing fee-bearing has settled yet.

The exchange

GPU capacity, priced and traded like power.

Every window that clears prints a price for one instrument. Physical first. The cash-settled market comes once the tape is long enough to price it.

not launched, maturing the benchmark right now

Under development · simulated data$/GPU-hr · 256-GPU lots · 3-month equivalent-:-:-
SL-H100-256-IB-US-3M · term mark · daily
$2.95+16.4% 1MTerm mark · 6 prints
2.502.753.00Aug 8SepOrnn 3.07Silicon Data 2.622.95
— the assessment, one price a day┄ trend┄ published index level
How the price gets madeLeft to right. Each stage feeds the next.
  1. RFQ links
    Building

    Brokers send our link instead of a WhatsApp message. The quote comes back through it.

  2. Feed
    Building

    Replies normalised to one instrument, with the source on each print.

  3. Benchmark
    Next

    The published term mark per SKU.

  4. Venue
    Prototype

    The monthly auction where blocks change hands.

  5. Market makers
    In discussion

    Two-sided firm quotes at every auction.

Advisory

A compute desk for the companies that hold compute.

If your GPU bill floats and your prices do not, you hold a position in compute. We read the contracts and tell you what to sell, what to buy short, and what to leave alone.

Inference companiesData centres and neocloudsAI labsRobotics labsEnterprises with reserved capacity

The first review is free.

01Your book

Every contract, tenor and price on one page.

02Your windows

The months you will not use, and what they clear for.

03Your exposure

What a 40% move in GPU rates does to your margin.

04Your next buy

Sign long, take a window, or wait a quarter.

Why us

We have run an exchange before.

A prediction market is an exchange with the hard parts left in. We built and ran India’s second-largest one.

Arpit Dhamija

Markets, product and business. Founded India's second-largest prediction-market platform. Engineering at Amazon and Adobe.

Aayush Agarwal

Ex-quant at one of India's largest fintechs; founded prediction-market managed baskets. CFA Level 3.

Siddharth Singhal

Capital markets and distribution. An L1 builder, now at Tezos in London. Berkeley and INSEAD.

Justin Brewer

Markets and trading. Ex-trader and partner at a Chicago trading firm. Institutional market infrastructure.

Previously at
AmazonAdobeGrowwInMobiTezosStanfordUniversity of California, BerkeleyINSEAD

Nothing here is a derivative.

Physical delivery only. No cash settlement, no net-out. The analogue is electricity, not oil.

Not Yet ;)