compute markets @Architect_Fi | prev @PanteraCapital opinions are my own

New York
Joined July 2020
Pinned Tweet
We're starting a Telegram community to get folks in the compute capital markets together. DM if you'd like to join!
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Today our Founder & CEO @BrettHarrison joins the @CFTC's inaugural Frontier Forum on super intelligence and agentic finance. 1:00–4:00 pm ET. Watch live: cftc.gov/Webcast/current
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hey guys, understand the security struggle atm (and the sleep deprivation) we’re taking requests for access to abliterated variant of DeepSeek V4.1 Flash 1017 for defensive security testing (others avail too) if interested - DM me your project and use case link in 🧵
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If your organization is using OSS Models for internal use or needs inference for your product, this should be your go-to website. 1. Compare live offers from white-listed providers 2. Request for inference quotes (fixed rates and term lengths) 3. Match up! An example: Request 6-months of fixed price on GLM 5.3 capped at 100B tokens/day at $X/mToken.
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Liquid Inference has copy-paste setup for 21 tools, including Claude Code, Codex, Cline, OpenCode, LiteLLM, LangChain and the Vercel AI SDK. For Claude Code it's two environment variables.
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Excited to see @jetinfer joining @Architect_Fi's inference provider network! We're focused on delivering fast, cost-efficient inference for open-weight models, and it's great to collaborate with teams pushing this ecosystem forward. Thanks to Brett and the Architect team for having us on board! 🚀
The Liquid Inference router network is growing quickly. We now have 28 verified companies serving >1000 models, offering capacity dynamically and updating quotes in real-time. Average time for providers to onboard and integrate models is 3 hours, full verification within a day.
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The experience is so seamless that you only need a few clicks to get ready.
Onboarding to Liquid Inference takes 3 easy steps: 1. Fund your workspace - new accounts start with free credit 2. Make a Preset under Rules once, use it forever 3. Send your first prompt and compare costs and model output side by side
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We're also giving out credits to early users providing the team with feedback
Onboarding to Liquid Inference takes 3 easy steps: 1. Fund your workspace - new accounts start with free credit 2. Make a Preset under Rules once, use it forever 3. Send your first prompt and compare costs and model output side by side
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We have a few providers who want to offer 3 to 6-month fixed-rate inference on open-weight models to your enterprise. ZDR and some are SOC-2 compliant. DM for intros.
We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case). I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves. We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them: "/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started" I imagine we'll have some backend services that could be replaced entirely by this new architecture.
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Another provider who started quoting yst and we're seeing markets do their thing ✨
The response to LI has been overwhelmingly positive. I had a first call with a new inference provider at 5pm ET and they're live and quoting on the platform. Wild.
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inference provider cartel hates to see LI coming
It’s been one day since the launch of Liquid Inference and we’ve doubled our provider network. We’re cheaper than OpenRouter for Kimi K3, DeepSeek V4.1 Flash, Qwen3.8 27B, and ~30 more models. Every new provider that joins increases price competition.
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The response to LI has been overwhelmingly positive. I had a first call with a new inference provider at 5pm ET and they're live and quoting on the platform. Wild.
Today we’re launching Liquid Inference, an OpenAI/Anthropic-compatible router where inference providers compete on price for every prompt. Buyers route across 700+ open- and closed-weight models at the lowest available marginal cost, providers onboard instantly to start serving models.
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Boundless is proud to be a launch partner and inference provider on @Architect_Fi’s Liquid Inference router. Another way for builders to access low-cost inference. Congrats to the Architect team on the launch 🫡
Today we’re launching Liquid Inference, an OpenAI/Anthropic-compatible router where inference providers compete on price for every prompt. Buyers route across 700+ open- and closed-weight models at the lowest available marginal cost, providers onboard instantly to start serving models.
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Architect tried perpetuals first. It didn't work for compute. Ishanee Nagpurkar @Ishanee, Head of Compute Markets at Architect @Architect_Fi, explains why they moved to futures instead: a contract that lets you lock in a price today, say, $2.50 for an H100 six months out, and settles the difference in cash if the price moves by then. Full episode available now.
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Ish retweeted
We made an interactive explainer of the AI compute stack: from grid power and data centers to chips, racks, clusters, and workloads, plus the public companies at each layer. Explore the infrastructure behind what @JensenHuang calls the five layer cake: intelligence.mts.now/compute
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At the current rate of compute financialization, CDOs on GPU-backed leases are likely coming to market. Non-investment-grade neocloud operators with 8-9 figure budgets are struggling to secure financing for under double-digit interest rates, and bundling is a short-term solution as a liquid residual value curve develops. A GPU-backed CDO could be structured as a portfolio of GPU leases across operators, chip generations, and geographic locations. The tranches could be • Senior notes sized to contracted utilization and investment-grade offtakers • Mezzanine notes that take on contract renewal risk • Subordinated/equity notes that absorb residual value depreciation Ratings agencies, including startups specializing in the assessment of compute quality, face the challenge of modeling default correlation with very little historical data. Any analysis would need to take into account offtaker credit-worthiness, GPU-hour rental prices, new accelerator manufacturing, memory and electricity costs, and political factors affecting buildouts. Listed compute futures and options are the solution the US financial system is waiting for, but that shouldn’t prevent financial institutions from deploying every available innovation to address the growing credit crisis, even if inputs are nascent.
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Architect is hiring for three lateral roles: options trading quantitative researcher, compute capital market sales, and structured derivatives specialist. Details on what we’re building and the new team members we’re looking for Quantitative Researcher: We’re building: automated high-frequency options trading strategies on legacy and new(er) exchanges What you’ll work on: volatility modeling, ML for parameter optimization, order placement simulation, new venue exploration Stack: Rust, Python, AWS, ClickHouse What we’re looking for: field experience in derivatives trading and research, strong system design instincts, multiple approaches to options pricing theory, bias towards shipping Nice to have: familiarity with the latest ML toolkits and exchange microstructure Location: your preference Compute Capital Markets Sales: We’re building: the first US-regulated exchange purpose-built for compute futures, options, swaps, forwards, and capacity, plus AI supply chain commodities What you’ll work on: building relationships and facilitating trades with neoclouds, lenders, insurance providers, inference companies, datacenters, AI labs, interdealer brokers, retail brokers, hedge funds, and asset managers What we’re looking for: network of established and startup AI companies, fluency with compute capacity pricing/structures, translation skills between finance and AI, aversion to slop, commitment to competitive markets for compute Nice to have: experience in compute procurement at a hyperscaler, neocloud, or lab Location: SF Structured Derivatives Specialist: We’re building: specialized contracts for trading and hedging compute, compute derivatives, and a range of physical commodities supporting the US datacenter build-out What you’ll work on: modeling and structuring OTC products with Architect’s traders/technologists and AI counterparties for compute, memory, inference, and energy trading What we’re looking for: experience trading OTC derivatives contracts at banks or trading firms, sell-side institutional network, enthusiasm for working at startups Nice to have: relevant FINRA/NFA certifications or passion for test-taking Location: your preference
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Late to this but is @meetgranola the new CRM-killer
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TIL: Fidelity was one of the biggest beneficiaries (and participant) of the Go-Go Years back in the 60s
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Compute Markets referenced a couple of times in today's committee meeting. Everyone agrees that these markets should exist and fast-tracked. TLDR: @FalconXGlobal @2Ragu - AI CapEx hit $670B in a single-year. These will be the largest markets ever and an opportunity for the CFTC to define compute as an asset class. AI Labs going through IPO processes are being asked (for the first time) about their compute or datacentre expenses and they want to find ways to hedge to reduce their cost of capital. @multicoin @tushar_jain requesting for an innovation exemption for compute derivatives. @blockchain doubles down on Multicoin's idea for a sandbox environment for compute. Don Wilson says compute markets gives US capital efficiency in the AI race. Duffy highlights that Kalshi's compute markets are live whilst everyone else must abide by CFTC's request for comment period.
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