Stoa Brings Structured RFQ Trading to the GPU and AI Server Market
Stoa, a marketplace for new and used GPUs and AI servers, has emerged from Y Combinator's Summer 2026 batch with an approach that borrows heavily from institutional trading infrastructure. The platform lets buyers describe hardware in plain language or attach a quote PDF, after which an AI drafts a request for quote (RFQ) that the buyer can edit before it goes out to verified dealers. Those dealers respond with firm quotes, and accepting a quote creates a binding trade. Stoa
Founded by Eren, Berat, and Kaan, Stoa grew out of the founders' own GPU brokering business. They entered brokering to understand firsthand why GPU hardware trading remained manual and inefficient, and the marketplace is the productized result of that experience. The three founders have known each other for over ten years and bring backgrounds in founding companies, trading interest rate derivatives, and building trading and pricing systems for oil and gas. Hacker News
The RFQ marketplace covers datacenter GPUs ranging from bare cards to full clusters, in new or secondary condition. Before dealers can receive requests for quotes, they must pass know-your-business (KYB) checks. On the buyer side, the RFQ process requires confirming exact configuration, quantity, condition, warranty, location, delivery terms, and inspection criteria before dealers return firm quotes against the same request. Stoa does not take possession of the hardware it brokers. Hacker News
Settlement runs on a recorded timeline of steps: confirmed, payment, shipped, delivered, inspected, and settled. Each step requires evidence along the way, creating an auditable trail from order acceptance to final settlement. Stoa charges a tiered fee on completed trades, with lower fees at higher volumes. Stoa
In its first month, Stoa received more than $300 million in requests for quotes. The company is a member of Y Combinator's S26 batch, confirmed by YC's own LinkedIn announcement in early August. Hacker News Y Combinator
The GPU secondary market has been characterized by opaque pricing, trust gaps between counterparties, and settlement risk. What Stoa is attempting is structurally familiar to anyone who has worked in electronic bond or commodities trading: standardize the RFQ workflow, enforce counterparty verification, and create a binding settlement chain with evidence at each step. The founders' derivatives and oil-and-gas pricing backgrounds are visible in the design choices, particularly the tiered fee structure tied to completed trades rather than listings or inquiries, and the evidence-gated settlement timeline.
The AI-drafted RFQ is worth noting as a practical application of large language models in B2B procurement rather than a novelty. A buyer attaching a vendor quote PDF and receiving a structured, editable RFQ back compresses what is typically a manual specification step, one that has historically been a source of errors and disputes in hardware procurement. Whether the AI-generated drafts prove reliable enough at scale to reduce, rather than introduce, friction will depend on how well the system handles the long tail of GPU configurations, firmware revisions, and cluster-level requirements that buyers care about.
The $300 million in first-month RFQ volume signals demand for a structured marketplace in this category, though volume of requests is not the same as completed and settled trades. The gap between RFQ volume and actual transaction throughput will be the metric to watch as the platform matures.
Stoa's decision not to take possession of hardware keeps it asset-light but places the burden of logistics, insurance, and physical verification squarely on the counterparties and the settlement workflow. The evidence-gated timeline is the mechanism that is supposed to hold this together: if each step requires proof before advancing, the system creates accountability without Stoa needing to operate as a warehouse or logistics provider.
The broader question for platforms like Stoa is whether marketplace liquidity concentrates fast enough to make the RFQ model the default trading venue for GPU hardware, or whether the secondary market fragments across informal channels, direct broker relationships, and competing platforms. The tiered fee structure is designed to reward volume and concentrate trading, but network effects in hardware marketplaces have historically been slower to compound than in purely digital goods markets, where settlement is instantaneous and physical logistics are absent. GPU trading sits in an awkward middle: the assets are high-value, physically delivered, and configuration-sensitive, which raises the stakes on every step of the settlement chain.
For now, Stoa has built the rails. The market will determine how much traffic runs on them.
