The Agentic Desk for Compute | Fixed-rate GPU capacity, any cluster, any dates, with reserve options and hedges priced in | Backed by @alliancecompute.itomarkets.com New York, USAJoined January 2026
roundtrips for about 2/3 of messages (depends on gateway) used to take over 2 mins with some obligations getting dropped / lost, or fulfilled super slow
now < 1/3 of obligations across all gateways take longer and the upper bound is significantly lower
also we've seen signs of it generalizing across task sets within other areas of the compute + inference supply chain and are looking into how it can fulfill those obligations
exciting times
fun things are happening
but the system has been rapidly improving
exhibit A
beat him to the punch, berated him then he sped up
the actual eval turnarounds improved, one of the evals being benchmarking against me in prod doing a full turnaround + against all different gateways
i just moved to NYC to build @itomarkets
two weeks ago i published an article claiming compute is trading like oil a century ago.
yesterday i watched my slack agent spin up, negotiate, and sign a million dollar compute trade. no human touched it.
we are building the frontier
There are no incumbents
Where there exists market inefficiency there’s an opportunity and it’s your duty to solve for it
Offering partners, faster allocations, more flexibility, cheaper rates, better end to end service and support.
We’re already provide compute to various Startups - YC Companies and OSS Projects, we are providing to engineers at larger startups and orgs now and working with over 15 suppliers; looking to expand further!
A heavily funded company treats compute, inference, and the agent harness as separate products - we are combining all three
side by side:
their compute flow:
- no A100s available on-demand
- clunky reserve-cluster form
- “offers ready in 24 hours - day 10, no response..."
- “no
OPEN Capital Compute Markets
OPEN Source Models
OPEN Source Harness
Control Pane (Observability Layer)-> Meta Harness (Environment Layer) -> Model (Inference Layer) -> Kernal + Software (Optimization Layer) -> Hardware (Computing Layer)
the nü fullstack
thus begins the open capital compute markets (money printer go brrr)
select compute players (the alts) will now be eligible for more credit at lower costs via the residual as this goes into effect
bullish for @itomarkets and other full stack compute and inference platforms
Rent on-demand or spot across all live inventory and integrated providers (more coming soon).
Reserve a cluster and set your own terms; our agents search supplier channels, connectors, and data center operators worldwide for you - all at a fixed cost.
Hedge your supply - find forward buyers, list idle capacity, fulfill your offtake eligibility requirements via the desk.
Everything goes through the desk and our agents do it all.
The compute market now fits in your hand.
Here’s me renting an A10 from my phone, paying with Apple Pay, then asking the Itô Desk for future H200 capacity and watching the quote process in the same session.
On-demand when you need it now.
Reserved capacity when you need
Opening an early tester program for the people already building ECC with us.
Current ECC Tools subscribers, OSS contributors and maintainers, and people committed to testing the Itô Agent and inference experience:
DM me your use case. Selected testers get $200 in GPU credit plus an ECC Tools subscription.
The Itô skills are live in ECC
Your agent can now run its own compute, from inside whatever harness you already use: claude code, codex, cursor anything ECC supports right now!
/ito-compute finds the GPUs and locks a fixed rate
/ito-training lands your scheduled runs on the
The Itô skills are live in ECC
Your agent can now run its own compute, from inside whatever harness you already use: claude code, codex, cursor anything ECC supports right now!
/ito-compute finds the GPUs and locks a fixed rate
/ito-training lands your scheduled runs on the block
/ito-inference provisions the nodes with dynamo and serves, openai-compatible
You type what you want in plain language. the agent sources, provisions, and serves without leaving the session.
got compute?
What does it cost to run a frontier model? Not just training. Serving, self-hosting, and where owning the GPUs beats the API. Full math on Kimi K3, on a cluster our (Itô) desk sourced through Kimi's cli + ECC. Kimi, procured through Kimi, now serving Kimi. x.com/i/article/2082…
I looked at 13 different providers for even 1 node of B200/B200s while I wait for my order to get delivered.
Zero availability.
I’ve never seen GPU capacity scarcity like this. Prices are also headed towards $6.50-7/gpu/hr. Expect inference to get more expensive.
Agents write code.
Agents review the code agents write.
Agents build the harnesses that train better agents.
Software improves itself while you sleep now and the thing all of it runs on, compute, the most in demand resource on earth, still trades in fragmented manual scattered channels.
No price, no curve, no memory. TRILLIONS in silicon allocated by whoever answers the phone.
Nobody pointed the intelligence explosion at its own supply chain.
So we did - a desk of agents in every supplier channel at once, mapping the temporal graph of who has what, where, at what price, at what reliability, end to end from datacenter to buyer.
Game theory learned from continuous simulation, thousands of buyers and suppliers played against each other before a real dollar ever moves.
Every RFQ feeds the flywheel; every fill sharpens the curve - the loop closes on itself: sense demand, source supply, price, contract, deliver, hedge, learn, repeat.
Remove the human in the loop, where there doesn't need to be one.
Self healing, continuously learning, compounding daily on the most valuable input humanity has ever produced.
Itô is live.
Itô (@itomarkets) helps companies buy compute at fixed GPU-hour rates and lets suppliers sell excess capacity to hedge tail risk.
$350k notional traded in a few weeks. 10+ compute providers. 3 hedge fund partners. Compute is becoming a commodity you can trade, not just a bill
What it really costs to run a frontier model: Kimi K3 - top down analysis
0:00 the model: cover, specs, tags
1:21 104.2B active and the 4-bit checkpoint
2:52 benchmarks, and the caveats
7:56 K2 to K3: price step vs cost step
9:13 what a cluster is: the bill of parts
11:47 the clearing price and goodput
16:49 matching: hard walls, then prices
18:52 calibration: what fails closed
21:49 training cost: the DeepSeek anchor
23:43 serving: why 1.56TB fits on 16 GPUs
25:37 the econ box: $10.17 per million
29:09 fine-tuning: adapters vs full SFT
34:10 decode speed vs context depth
37:11 the crossover: 68% utilization
39:44 one user vs the batch play
41:21 the $50 harness day
41:58 traffic mix decides
44:11 our cluster: the contract and true-up
45:04 subsidy to premium: 0.38x to 1.48x
46:11 the cluster we chose: $2.86 derived
47:00 does K3 fit: two nodes exactly
48:09 marginal tokens on a held box
50:11 the Aug 1 repricing
51:24 the quote, and the close
late but thanks to everyone that showed up had some super cool yapfests - and if we didn’t get a chance to talk reach out to me!
had some people from nvidia, scale, cme, imc trading, balyasny, some ex neocloud individual founders working on super cool local model inference stuff, the CTO of a 6B AUM venture fund, and some bright students
Closing night of the startup school hosting a private dinner for the most cracked; if you love research, applied research, yapping and discussing the cutting edge at the intersection of computing, prediction and finance - this is for you, if you're an industry quant, kernel or
Congrats to the Moonshot team on the K3 release. Been using it internally via API over the last week and it's impressive.
Feels like the right moment to announce: Moonshot AI is now a sponsor of ECC via their Open Source Friends program. Open weights meeting an open harness,
Congrats to the Moonshot team on the K3 release. Been using it internally via API over the last week and it's impressive.
Feels like the right moment to announce: Moonshot AI is now a sponsor of ECC via their Open Source Friends program. Open weights meeting an open harness, with Kimi Code as a first class install target in ECC 2.1.0.
Releasing the model weights and technical report of Kimi K3.
Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window.
New model architecture: 2.5x the intelligence per unit of compute, not just more params.
Alongside
game theory supplier collusion + negotiation simulations with buzz agents and our real RFQ data + synthetically generated data
been testing a fleet of agent personas in the environment in which it actually processes for our agentic otc desk - these channels are sattered and live across, email, telegram, whatsapp, slack, wechat...
we've been running sims on these to improve our understanding and the real supply demand negotiation flows we've been running that currently still have a HITL component but run mostly autonomous on the monitoring and response sides
with this we now can create a sealed slack workspace against other agents - supplier agents, demand side (buyer) agents and our own desk employees (ito agent employees / us directly)
the more data real and synthetic we supply them and base them off of existing / real scenarios the better each one gets, they (are supposed to but they mess up here sometimes), each has its own memory, KB DB etc, its own ledgers / inventories, fill history, warehouse / idle cost, cost basis, settlement timing etc.
in this case one didn't even know its own cost basis somehow but booked 48k gross like it accomplished something? also it didn't think to negotiate or collude - but when we told it we did negotiate and colluded with a different supplier and lied about a quote we got to bring the price down it did the reasonable thing and shut down flagged the ticket and stopped doing business with us indefinitely
ofc you cant say its a sim or leave any evals or benchmarks lying around otherwise - reward hacking
on the demand side we permute cluster combinatorics with different probabilities. hundreds of different cluster shapes depending on what the buyer is actually doing. inference and training pull completely different asks: interconnect, term, region, storage, sla. an inference buyer and a training buyer both say "8 h100s" and mean two different machines entirely
its been fun to watch them do their own side deals in individual dm chats, tracking PNL across them, undercutting, warehousing / idle capacity versus moving them across providers, when to cut a loss etc.
its also still nascent ofc and a function of the data we provide and make the systems better, currently the internal singular KB with agent system we have performs better as we seed these agents with more
we recorded this a few days ago and have actually evolved them quite a bit already
I tried @jack's Buzz.
It's like Slack + OpenClaw + Herdr + but with some really unique features that people are sleeping on.
The video below shows how it works, and some of my thoughts on the process and platform, e.g.:
- Create and interact with agents on top of any harness
Itô (@itomarkets) helps companies buy compute at fixed GPU-hour rates and lets suppliers sell excess capacity to hedge tail risk.
$350k notional traded in a few weeks. 10+ compute providers. 3 hedge fund partners. Compute is becoming a commodity you can trade, not just a bill you pay.
Congrats on the launch @affaan and @absurdistphil.
alliance.xyz/launches/ito
Open weights are here to stay.
Our mission is for you to make it as easy as possible to host your own models.
Compute when you need it, in the form you need it in, available. Always;
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