Aivres Blackwell lineup: KR9288X3 and HGX B200 systems.

When a buyer needs three HGX B200 nodes, almost any OEM on the qualified list will do. When a buyer needs three hundred of them, on a schedule, with consistent firmware and validation across every chassis, the field narrows fast. Volume is its own engineering discipline, and it is the lens through which Aivres earns its place in the Blackwell conversation. The KR9288X3 is a competent 8-GPU HGX B200 server on the spec sheet. What makes it interesting to a hyperscale or large-cloud buyer is the factory behind it.

Aivres Systems is the former Inspur Systems, and it still operates inside the Inspur group as a high-volume server manufacturer. That lineage matters because it grounds the differentiator we care about here: this is a vendor built around hyperscale-grade throughput, not boutique builds. For a buyer assembling a multi-rack Blackwell fleet, supply reliability and rack integration discipline are not soft attributes. They are the difference between a deployment that lands in one delivery window and one that dribbles in across three quarters. Aivres is a qualified OEM ecosystem member that Rillor lists and can source from, not an announced or signed Rillor partner, and everything below is read through that lens.

The KR9288X3 at the node level

The KR9288X3 is a 10U rack server built around an NVIDIA HGX B200 8-GPU baseboard. Each of the eight Blackwell GPUs carries 180GB of HBM3e, putting 1.4TB of GPU memory on the board, and the GPUs run at a TDP of up to 1000W each. That power draw is the single fact that drives every other decision in the chassis, from the 10U height to the cooling choice to the power-shelf layout in the rack.

Two Intel Xeon 6 (Granite Rapids AP) processors anchor the head node, with TDP up to 350W per socket, feeding 32 DDR5-5600 RDIMM slots. Aivres exposes up to 14 PCIe 5.0 x16 slots, which is what makes the system flexible at fleet scale: that slot budget is where the network fabric lives, and on a B200 build it carries 8 ConnectX-7 400G NDR adapters for the east-west GPU fabric, with BlueField-3 DPU support available for the management and storage planes.

At the baseboard level the numbers come straight from NVIDIA's HGX platform. The HGX B200 8-GPU board delivers 14.4 TB/s of total NVLink bandwidth, 1.8 TB/s GPU-to-GPU via the NVLink 5 switch, and 144 PFLOPS of FP4 (sparse) or 72 PFLOPS of FP8 Tensor Core compute. Those are the figures every B200 NVL8 system shares, which is exactly the point of standardization: a buyer comparing the KR9288X3 to a Supermicro SYS-A22GA-NBRT or a Dell PowerEdge XE9680L is comparing chassis engineering, supply reliability, and integration, not baseboard performance.

B300 and the Blackwell Ultra step up

The KR9288 platform is not a single SKU. The same chassis family supports both HGX B200 (Blackwell) and HGX B300 (Blackwell Ultra) 8-GPU modules, and the choice between them is the most consequential one a buyer makes on this platform.

On the B300 configuration, total HBM3e climbs to 2.3TB with 64 TB/s of GPU memory bandwidth, and NVIDIA's HGX baseboard figure puts total GPU memory at 2.1TB for the reference B300 board. The B300 build also moves the fabric up a tier: 8 ConnectX-8 SuperNICs deliver 800G per port instead of the 400G NDR on the B200, doubling per-node injection bandwidth into the cluster fabric. For training runs that are memory-capacity bound, or inference fleets serving very long context windows, that extra HBM headroom is frequently the deciding factor, and it is worth reading our B200 versus B300 breakdown before locking a configuration.

Head-node pairing is also where the B300 platform gives buyers a real choice. The KR9288 line takes either two Intel Xeon 6 (Granite Rapids) or two AMD EPYC 9005 (Turin) processors. That is not a cosmetic option. Turin's core count and memory-channel layout suit some data-loading and preprocessing profiles, while Granite Rapids AP can win on per-core throughput and certain AVX-heavy preprocessing paths. We unpack the tradeoff in Granite Rapids versus EPYC Turin for GPU server head nodes; for fleet buyers, the practical upshot is that Aivres lets you standardize the GPU plane while tuning the CPU plane to the workload.

The naming follows a clean convention across the air-cooled family. The Intel B200 build is the KR9288-X3, the AMD build is the KR9288-E3, and the liquid-cooled siblings sit in the KR5288 family. A buyer can therefore source an Intel HGX B200 node, an AMD HGX B200 node, or an HGX B300 node from one manufacturer with shared firmware and validation lineage, which is precisely the kind of consolidation that pays off at hundreds of units.

Air versus liquid, and the path to rack scale

Aivres ships the KR9288 family as air-cooled 10U chassis and the KR5288 family as direct-liquid-cooled. At 8 GPUs near 1000W each plus two 350W CPUs, an HGX node is pushing roughly 10kW before storage and networking overhead, and that number decides whether a given data hall can take air-cooled nodes at density or needs DLC. The board itself is the same Blackwell baseboard either way. What changes is the facility-side coordination, the rack power envelope, and the achievable nodes-per-rack. We cover that decision tree in air versus direct-liquid cooling for Blackwell systems, and it is the first question a buyer should resolve, because it constrains every OEM choice downstream.

The natural endpoint of the liquid-cooled path is rack-scale. The NVIDIA GB200 NVL72 connects 72 Blackwell GPUs and 36 Grace CPUs in a single liquid-cooled rack with 130 TB/s of NVLink bandwidth, where the whole rack behaves as one GPU and delivers up to 30x faster real-time trillion-parameter inference and 4x faster training versus H100. The compute tray uses NVIDIA's MGX design, with two Grace CPUs and four Blackwell GPUs per tray, and the 72-GPU NVLink domain is what lets the rack present itself as a single accelerator to the training job. For buyers who need that topology rather than discrete 8-GPU nodes, the rack-scale path is the one to spec, and we walk the GB200-versus-GB300 decision in GB200 NVL72 versus GB300 NVL72 at rack scale.

At rack scale the fabric also changes shape. Discrete HGX nodes connect over Quantum-2 QM9700 InfiniBand or Spectrum-X SN5600 Ethernet, while NVL72 racks scale out over the Quantum-X800 InfiniBand and Spectrum-X800 fabric. The choice between InfiniBand and Ethernet is its own multi-rack decision, and we treat it in full in Quantum-X800 versus Spectrum-X networking.

What actually differentiates Aivres

Spec sheets converge because the HGX baseboard is the same across every OEM. The differentiation lives in the factory and the support model, and this is where the hyperscale-volume thesis is genuine rather than marketing.

Aivres is headquartered with manufacturing in Silicon Valley and runs a Joint Design Manufacturing (JDM) model out of automated facilities. Every server is subjected to more than 2,000 full-life-cycle tests across nine test categories, and the company cites manufacturing-to-deployment turnaround as fast as eight days. For a buyer who has lived through a Blackwell deployment, those three facts translate directly into the metrics that matter: consistency across a large batch, low DOA and infant-mortality rates because of the test volume, and a turnaround number that makes large-quantity scheduling believable rather than aspirational.

The Inspur lineage reinforces this. Aivres Systems is the former Inspur Systems, and that history is precisely why the high-volume framing holds up. This is a manufacturer whose entire operating model is built around producing servers at scale, which is exactly what a hyperscale or large-cloud buyer is buying when they choose Aivres over a lower-volume integrator. It is also why the guardrail matters: Aivres is a qualified OEM in the ecosystem Rillor lists, sourced on the merits, not a signed or announced Rillor partnership.

For reference, indicative reseller pricing puts the Aivres HGX B200 system around $340,000 USD to start and the HGX B300 system around $430,000 USD, depending on final configuration. Treat those as starting points: real fleet pricing moves with quantity, cooling, fabric, and delivery window, which is exactly what a forward contract is built to fix in place. A 64-node order does not transact at single-unit list, and the delivery month is as negotiable as the price.

Mapping the lineup onto Rillor

On Rillor, the messy reality of OEM SKUs collapses into standardized contracts. The point of a forward market is that a buyer commits to a delivery month at a transparent price without first having to win an allocation conversation with each OEM, and the standardized forwards versus bespoke supply agreements distinction is what makes that possible.

A standardized SKU is what makes a forward contract on these systems possible in the first place. When RIL-GX-B200-2T trades, the buyer is not contracting on a specific Aivres serial number. They are contracting on a defined HGX B200 NVL8 specification with physical delivery, a 10% deposit at execution held by an independent escrow agent, the balance due at delivery, and a seller performance bond standing behind the seller's obligation. NVIDIA channel compliance is built into the contract, with the end customer of record captured so the transaction stays inside the channel rules. Rillor settles every contract physically and never cash-settles, which is the structural commitment we explain in why Rillor settles physically and never cash-settles.

Each active contract also feeds the Rillor Compute Index, the 30-day rolling-blend forward price per SKU that Rillor owns and licenses to exchanges, funds, and researchers as a settlement feed. The index is downstream of real contracts on real systems, which is why standardized SKUs like RIL-GX-B200-2T are the foundation it is built on. Buyers can browse the live tape on the marketplace, review the full SKU catalog, or see how a high-volume buyer plans around forward delivery on the for-buyers page.

For a buyer sourcing HGX nodes at volume, the practical sequence is short. Resolve cooling first, because it constrains the OEM field. Pick the GPU generation against your memory-and-bandwidth ceiling. Choose the CPU plane against your data path. Then commit the delivery month on the forward curve rather than racing spot. Aivres fits that sequence well precisely because its scale lets it deliver a large, consistent batch into a single window, which is the hardest part to guarantee any other way.