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Two air-cooled 8U systems can look identical in the rack and still behave very differently once real jobs arrive. That is the case with the ASRock Rack HGX B300 pair. The 8U16X-GNR2 and the 8U16X-TURIN2 carry the same eight Blackwell Ultra GPUs, yet they run on very different host platforms.
If you are running a GPU server comparison before buying new HGX B300 systems, skip the GPU tray. Both systems share it. The real answer sits in the CPU platform, the memory layout, and the way you plan to deploy. Get those three correct, and the rest of the build falls into place. Get them wrong, and you may spend months working around a host platform that never quite matched your software.
What Both Systems Share
All HGX B300 servers from ASRock Rack start with the same foundation, and these two are no exception:
- GPUs: Eight NVIDIA B300 SXM modules on an HGX NVL8 baseboard, linked by NVLink
- GPU memory: 288GB HBM3e per GPU, or 2,304GB per system
- Networking: Eight 800Gb/s OSFP ports through NVIDIA ConnectX-8 SuperNICs, one per GPU
- Storage: 12 hot-swap 2.5-inch NVMe bays (PCIe 5.0 x4)
- Expansion: Four FHHL PCIe 5.0 x16 slots
- Power and cooling: 6+6 redundant 3000W 80 PLUS Titanium supplies, air-cooled in an 8U chassis
Because the base is shared, the choice between these ASRock Rack servers comes down to the host side. That means GPU throughput per node will not separate them. For a closer look at the GPU platform itself, read our guide to HGX B300 architecture and enterprise use cases.
8U16X-GNR2 vs 8U16X-TURIN2: Side-by-Side Comparison
The table below shows where the two systems differ and where they match.
| Feature | 8U16X-GNR2 B300 | 8U16X-TURIN2 B300 |
| CPU platform | Intel Xeon 6 | AMD EPYC |
| Sockets | Dual Socket E2 (LGA 4710) | Dual Socket SP5 (LGA 6096) |
| Supported CPUs | Xeon 6700P, 6500P, 6700E | EPYC 9005 and 9004, including 3D V-Cache |
| GPUs | 8x HGX B300 NVL8 | 8x HGX B300 NVL8 |
| Memory slots | 16+16 DDR5 (2DPC) | 12+12 DDR5 (1DPC) |
| Memory types | RDIMM, RDIMM-3DS, MRDIMM | RDIMM, RDIMM-3DS |
| Max memory (Saitech listing) | 4TB | 6TB |
| Expansion | 4 FHHL PCIe 5.0 x16 | 4 FHHL PCIe 5.0 x16 |
| Front drive bays | 12 NVMe (PCIe 5.0 x4) | 12 NVMe (PCIe 5.0 x4) |
| M.2 support | 3 slots, PCIe 5.0 | 2 slots, PCIe 3.0 or SATA |
| Front networking | 8x OSFP 800Gb/s, 2x 1GbE | 8x OSFP 800Gb/s, 2x 1GbE |
| Power | 6+6 3000W Titanium | 6+6 3000W Titanium |
| Cooling | Air-cooled, 8U | Air-cooled, 8U |
Saitech lists both the 8U16X-GNR2 B300 and the 8U16X-TURIN2 B300 as special-order systems. Notice that most rows match. The differences cluster around the CPU, the DIMM layout, and M.2 storage.
One pattern stands out in the table. Everything that touches the GPUs, from NVLink to the 800Gb/s ports, is fixed by the HGX B300 baseboard. Everything that touches the host, from the CPU socket to the DIMM count, is where ASRock Rack gives you a real choice. That split is the lens for the rest of this comparison.
Intel vs AMD AI Servers: What the CPU Choice Changes
The CPU shapes memory design, software tuning, and host-side throughput. Here is how that plays out in practice.
CPU Platform
The 8U16X-GNR2 uses dual Intel Xeon 6 processors from the 6700P, 6500P, and 6700E series on Socket E2. Teams with Intel-tuned toolchains and validated Intel images often prefer it. See Intel’s latest Xeon platforms for AI infrastructure for the broader roadmap.
The 8U16X-TURIN2 takes dual AMD EPYC 9005 or 9004 processors on Socket SP5, including 3D V-Cache parts. It suits teams that want high core counts for data loading, preprocessing, and orchestration running beside the GPUs.
Memory Capacity
The GNR2 offers 32 DIMM slots at two DIMMs per channel and accepts MRDIMM modules for extra bandwidth. Saitech’s listing shows up to 4TB.
The TURIN2 has 24 slots at one DIMM per channel. Its listing shows up to 6TB, which works out to 256GB modules in every slot. In short, choose the GNR2 for bandwidth headroom and the TURIN2 for raw capacity. If your host-side jobs hold large datasets in RAM, that capacity gap is real.
Bandwidth matters as much as capacity. Two DIMMs per channel gives the GNR2 more flexibility in how you populate memory. One DIMM per channel on the TURIN2 typically keeps memory speeds higher. Ask your integrator to confirm supported DIMM speeds for your chosen CPU before you buy modules.
GPU Support and Expansion
GPU support is identical. Each system holds eight B300 GPUs connected over NVLink. Each also offers four FHHL PCIe 5.0 x16 slots for extra NICs, DPUs, or storage controllers.
Boot storage differs slightly. The GNR2 adds PCIe 5.0 M.2 slots, while the TURIN2 uses PCIe 3.0 or SATA M.2. Neither affects GPU performance. If you plan to add DPUs or storage cards, count your free slots first, because four fill up quickly.
Networking
Networking does not separate these systems either. Each exposes eight 800Gb/s OSFP ports on the front, one per GPU, plus two 1GbE ports for management and general traffic, so keep them off the training fabric. Plan cabling early, and match your fabric with NVIDIA Mellanox InfiniBand cables before the chassis arrives.
Cooling and Power
Both are air-cooled 8U systems with 6+6 redundant 3000W Titanium supplies. They need 208 to 230V AC input, so confirm your circuits first. Air cooling also means they fit racks that cannot support liquid loops.
Plan airflow as carefully as power. Eight B300 GPUs and two host CPUs produce serious heat, so use blanking panels, keep cold aisle supply steady, and avoid placing hot-exhaust gear in the same rack.
Which HGX B300 Server Is Better for Your Workload?
Any GPU server comparison at this level ends the same way: neither wins in every case. The table maps common workloads to the better fit.
| Workload | Better Fit | Why |
| Multi-node GPU training | Either | Identical GPUs and 8x 800Gb/s networking |
| CPU-heavy data pipelines | 8U16X-TURIN2 | EPYC core counts and 3D V-Cache options |
| Large in-memory datasets | 8U16X-TURIN2 | Up to 6TB listed memory |
| Bandwidth-sensitive host tasks | 8U16X-GNR2 | MRDIMM support and 2DPC layout |
| Intel-optimized inference stacks | 8U16X-GNR2 | Xeon 6 host with familiar tooling |
| Mixed fleets standardized on Intel | 8U16X-GNR2 | Simpler imaging and support |
Notice how many rows say either. For GPU-bound jobs, the two behave alike, so cost, availability, and existing vendor relationships can break the tie. Saitech’s current listings place the two systems at nearly the same price, so cost rarely decides it. Availability and your team’s operating experience usually do.
Deployment Scenarios
Large-Scale Training Clusters
For AI training servers, scaling across nodes matters most. With eight 800Gb/s ports per node, either system can anchor a multi-node cluster. Our guide to the best 8 GPU servers for enterprise AI model training shows how nodes like these compare with other options. Keep node-to-node bandwidth in mind as well. One 800Gb/s port per GPU lets collective operations such as all-reduce run without GPUs sharing a link.
Inference and Mixed Workloads
Inference differs from the work AI training servers handle. Teams often run tokenization, retrieval, and caching on the CPU beside the GPUs. The TURIN2’s larger memory ceiling helps when those tasks hold big datasets in RAM. The GNR2 helps when your runtime is tuned for Intel hardware. Serving several models on one node also puts pressure on host memory, so size it generously.
Air-Cooled Data Centers
Not every facility can support liquid cooling. These two systems fit existing air-cooled rows, which makes them practical for colocation and enterprise sites. Rack power density is still high, so check how many nodes each row can support. Pair them with rack power from Saitech’s PDU and UPS range.
B300 Server Configuration Checklist
Before you lock in a B300 server configuration, confirm these items:
- Populate every memory channel to get full bandwidth
- Size NVMe capacity for checkpoints and training datasets
- Check rack depth and rail compatibility
- Confirm circuits and PDU capacity before delivery
- Verify BIOS and firmware support for your chosen CPU
- Decide which PCIe slots go to storage, DPUs, or extra NICs
- Plan the 800Gb/s fabric, including switches, optics, and cables
Memory is the easiest place to under-buy. Browse DDR5 server memory early so your DIMM plan matches the platform you select. Matching modules to the CPU platform avoids slow speeds and mixed-population problems.
How Saitech Supports Your Deployment?
Saitech Inc. is an authorized ASRock Partner Reseller serving customers since 2002.
Custom Server Builds
Saitech configures custom servers around ASRock Rack servers, matching CPU, memory, storage, and networking to your workload.
Memory and Storage Sourcing
DDR5 memory and NVMe drives can be sourced alongside the chassis, so one order covers the full build, and compatibility questions stay with one team.
Quote Support
Send a request for quote with your CPU platform, memory target, and quantity. The team replies with pricing and lead times.
Deployment Guidance
Saitech helps you compare both platforms against your workload before you commit, covering CPU, memory, and fabric choices.
The Bottom Line
Choosing between Intel Xeon 6 and AMD EPYC comes down to your workload priorities, memory requirements, and long-term scaling plans. Evaluate each platform's strengths to determine which configuration best supports your AI training, inference, and high-performance computing needs.
Ready to move forward with your AI infrastructure? Contact Saitech Inc. for expert guidance, customized server configurations, and competitive quotes tailored to your project requirements. From selecting the right processor platform and configuring your eight B300 GPUs to finalizing memory capacity and high-speed cabling, we can help you align every component with your deployment goals. Get in touch today to discuss your requirements and take the next step toward a deployment-ready AI server solution.
