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ASRock 4U16X-GNR2 Packs 8 B300 GPUs, Demands Liquid Cooling

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ai servers

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ASRock Rack’s 4U16X-GNR2 shows how radically the definition of a “compact” AI server has changed. The platform compresses an eight-GPU NVIDIA HGX B300 system, two Intel Xeon 6 “Granite Rapids” processors, liquid cooling, high-speed networking, local NVMe storage, and redundant power into a 4U chassis—a formidable density proposition for enterprises building AI training, inference, and HPC clusters. ServeTheHome’s hands-on review positions it squarely in the market for organizations that need an established eight-GPU scale-up node rather than a loosely connected collection of accelerators.

This is not a server intended to sit in a conventional small-business rack, nor is it a product that makes sense as an isolated “AI appliance.” The ASRock Rack 4U16X-GNR2 is infrastructure: a dense compute building block designed to become one node in a carefully planned, liquid-cooled AI factory. Its appeal lies not merely in the Blackwell Ultra silicon, but in a system design that treats compute, GPU fabric, networking, storage, management, airflow, and serviceability as interdependent parts of one machine.

Overview: A Familiar Eight-GPU Formula, Updated for Blackwell Ultra​<br>At the center of the system is NVIDIA’s HGX B300 platform. This is the latest expression of the familiar eight-accelerator HGX concept: GPUs are mounted on a common baseboard and linked internally through NVIDIA’s NVLink and NVSwitch fabric, allowing them to behave more like a coordinated compute domain than eight isolated PCIe cards. NVIDIA describes HGX B300 as an eight-GPU platform using fifth-generation NVLink and NVSwitch, with up to 1.8 TB/s GPU-to-GPU bandwidth and 14.4 TB/s total aggregate fabric bandwidth . NVIDIA’s HGX B300 reference architecture also specifies up to 288GB of HBM3e per GPU , or 2.30TB of GPU memory per node .

That enormous pooled memory footprint matters as much as raw accelerator performance. Modern large-language-model workloads frequently become constrained by model size, context windows, key-value caches, batching requirements, and communication overhead—not just arithmetic throughput. An eight-GPU B300 node gives software a substantial high-bandwidth memory pool while retaining the low-latency links necessary for tensor parallelism, pipeline parallelism, and other multi-GPU execution methods.

NVIDIA’s own DGX B300 configuration illustrates the scale of the underlying platform: eight B300 GPUs, 2.3TB of total GPU memory, two fifth-generation NVLink interconnects, and up to 144 PFLOPS of FP4 inference performance . The DGX B300 documentation is not a specification sheet for ASRock Rack’s chassis, but it provides an important reference point for the capabilities and expectations surrounding the HGX B300 ecosystem.

ASRock Rack’s value is in bringing that common HGX foundation to a system that emphasizes configurable cooling, front-access connectivity, dense storage, and operational flexibility. It is the sort of hardware where a few apparently minor mechanical choices can materially affect rack deployment time, cable management, and service procedures.

Why 4U Density Is a Big Deal​<br>A 4U server may sound physically imposing in a world accustomed to 1U and 2U systems. In the AI infrastructure context, however, fitting eight flagship GPUs and their supporting CPUs, networking, switching, storage, cooling plumbing, fans, and power subsystems into 4U is remarkably dense.

The distinction becomes clearer when comparing liquid-cooled and air-cooled GPU designs. Air-cooled platforms need large heatsinks, massive airflow volumes, and substantial internal clearance. ASRock Rack also showed an air-cooled 8U16X-GNR2 B300 design for facilities that cannot deploy liquid cooling, underlining the physical advantage that direct liquid cooling can offer. ASRock Rack’s announcement of the 4U ZutaCore version explicitly contrasts the 4U liquid-cooled system with...