The G4L3-SD1-LAX5 is a 4U HGX AI server in the GPUMachines inventory. It is built for buyers who want configurable infrastructure rather than a one-size-fits-all appliance: CPU choice, memory population, storage layout, network adapters, and deployment model all matter as much as the base chassis.
HPC/AI Server - 5th/4th Gen Intel Xeon Scalable - 4U DP NVIDIA HGX™ B200 DLC. This server supports liquid-cooled NVIDIA HGX™ B200 with 8 x SXM GPUs for accelerating AI and HPC workloads.
The product-specific point to notice is direct liquid cooling, Blackwell B200 generation, Intel Xeon Scalable CPU platform. That combination changes the buying conversation from a generic server choice into a decision about rack density, thermal design, accelerator fit, data movement, and operational support.
This review looks at where the G4L3-SD1-LAX5 fits, what its specification means in practice, and how to configure it through GPUMachines for on-premise, hosted, leased, or cluster deployments.
Executive Summary
The G4L3-SD1-LAX5 is best suited to AI labs, enterprise platform teams, research groups, and service providers that need multi-GPU scale-up performance for training, fine-tuning, high-throughput inference, simulation, or private AI clusters.
The headline configuration story is 8 x NVIDIA B200 SXM GPUs on the NVIDIA HGX B200 platform, backed by 2 CPU socket(s), 32 DIMM slots, DDR5, 10 storage positions, and 12 PCIe expansion slots.
It is overkill for single-GPU development, occasional experiments, small inference endpoints, or teams that do not need high-speed GPU-to-GPU communication.
Start configuration here: configure the G4L3-SD1-LAX5 on GPUMachines.
Key Specifications
| Area | Specification | | --- | --- | | Form factor | 4U rackmount | | CPU platform | LGA4677 | | CPU sockets | 2 | | GPU support | 8 x NVIDIA B200 SXM GPUs on the NVIDIA HGX B200 platform | | Memory | 32 DIMM slots, DDR5 | | Storage | 8 x 2.5" Gen5 NVMe/SATA hot-swap bays (NVMe from PEX89104). 2 x M.2 slots (1x PCIe Gen3 x2, 1x PCIe Gen3 x1) from PCH. Supports Intel SATA RAID 0/1/10/5. | | PCIe expansion | 8 x FHHL x16 (Gen5 x16) from PEX89104, 4 x FHHL x16 (Gen5 x16) from PEX89048 | | Networking | Front: 2 x 10Gb/s LAN (1 x Intel X710-AT2), 1 x 10/100/1000 Mbps Management LAN. Rear: 1 x 10/100/1000 Mbps Management LAN. | | Power | 4+4 3000W 80 PLUS Titanium redundant power supplies. AC Input: 115-127V~/ 14.2A, 50-60Hz; 200-220V~/ 15.8A, 50-60Hz; 220-240V~/ 14.9A, 50-60Hz. DC Input (China only): 240Vdc/ 14A. DC Output: Max 1450W/ 115-127V~ (+54V/ 26.6A, +12Vsb/ 3A); Max 2900W/ 200-220V~ (+54V/ 53.4A, +12Vsb/ 3A); Max 3002.4W/ 220-240V~ or 240Vdc Input (+54V/ 55.6A, +12Vsb/ 3A). | | Best-fit workloads | LLM pre-training and fine-tuning; multi-GPU inference with tensor parallelism; large batch training; simulation and scientific computing | | Dimensions | 447 x 175.5 x 901 mm |
Platform Highlights
- GPU platform: 8 x NVIDIA B200 SXM GPUs on the NVIDIA HGX B200 platform. This matters because accelerator choice drives the rest of the configuration: CPU lanes, rack or chassis power, airflow, local storage, and network design.
- CPU and memory base: LGA4677 with 32 DIMM slots, DDR5. The right CPU and memory plan should be sized around data preparation, host-side model work, and how many accelerators or services need to be kept busy.
- Storage layout: 8 x 2.5" Gen5 NVMe/SATA hot-swap bays (NVMe from PEX89104). 2 x M.2 slots (1x PCIe Gen3 x2, 1x PCIe Gen3 x1) from PCH. Supports Intel SATA RAID 0/1/10/5.. Local NVMe is useful for active datasets, checkpoints, scratch space, and staging work before data moves to shared storage.
- Expansion and networking: 8 x FHHL x16 (Gen5 x16) from PEX89104, 4 x FHHL x16 (Gen5 x16) from PEX89048. NIC placement and PCIe lane planning are important when the system will connect to storage, other GPU nodes, or remote users.
- Power and cooling: 4+4 3000W 80 PLUS Titanium redundant power supplies. AC Input: 115-127V~/ 14.2A, 50-60Hz; 200-220V~/ 15.8A, 50-60Hz; 220-240V~/ 14.9A, 50-60Hz. DC Input (China only): 240Vdc/ 14A. DC Output: Max 1450W/ 115-127V~ (+54V/ 26.6A, +12Vsb/ 3A); Max 2900W/ 200-220V~ (+54V/ 53.4A, +12Vsb/ 3A); Max 3002.4W/ 220-240V~ or 240Vdc Input (+54V/ 55.6A, +12Vsb/ 3A).. Final power draw is configuration-dependent, especially once GPUs, NICs, and NVMe devices are selected.
- Product-specific fit: The product-specific point to notice is direct liquid cooling, Blackwell B200 generation, Intel Xeon Scalable CPU platform. That combination changes the buying conversation from a generic server choice into a decision about rack density, thermal design, accelerator fit, data movement, and operational support.
- Scale-up behaviour: HGX platforms are selected for high-speed GPU-to-GPU communication. NVLink and NVSwitch can be more important than raw GPU count when training, fine-tuning, or serving large models across multiple accelerators.
Our Technical View
In the GPUMachines portfolio, G4L3-SD1-LAX5 belongs in the top tier of scale-up AI systems. The reason to consider it is not simply that it can host many GPUs, but that the platform is designed for workloads where GPU-to-GPU communication, memory bandwidth, and predictable node behaviour matter.
This model is strongest when a team already understands that its workload can use a dense HGX node: LLM training, fine-tuning, high-throughput inference, simulation, or private AI cluster work. It may not be the best fit for early experimentation, occasional model runs, or teams that mostly need independent GPU workers. In those cases, a PCIe GPU server, workstation, or hosted GPU option may be more cost-effective.
The product-specific point to notice is direct liquid cooling, Blackwell B200 generation, Intel Xeon Scalable CPU platform. That combination changes the buying conversation from a generic server choice into a decision about rack density, thermal design, accelerator fit, data movement, and operational support.
Best-Fit Workloads
Best-fit workloads include:
- LLM pre-training and fine-tuning
- multi-GPU inference with tensor parallelism
- large batch training
- simulation and scientific computing
- private AI cluster deployments
- managed GPU hosting and Buy & Host infrastructure
Who Should Consider It
The G4L3-SD1-LAX5 makes sense when the project needs a properly specified infrastructure node, not just a part number. For AI teams, that usually means thinking through data movement, GPU or CPU utilisation, local scratch, shared storage, network fabric, and how the server will be operated after delivery.
It is most relevant for buyers that already understand their workload profile, have a target deployment model, and need help turning that requirement into a balanced hardware configuration. That may mean on-premise ownership, a hosted system, a leased deployment, or part of a larger private AI cluster.
Who Should Not Buy It
This is not the right first system for small AI inference workloads, single-GPU development, classroom experimentation, or teams that have not yet proven that their models benefit from dense multi-GPU scale-up. A smaller PCIe GPU server, 4-GPU server, workstation, or hosted GPU instance may be a better starting point.
Architecture Notes
The main reason to choose this class of system is not just GPU count. It is the scale-up behaviour inside the node. Workloads such as LLM training, fine-tuning, large-batch inference, simulation, and model-parallel jobs are often limited by the speed at which GPUs, CPUs, memory, storage, and network adapters can keep each other supplied.
For G4L3-SD1-LAX5, that means the surrounding configuration matters: CPU selection, DIMM population, local NVMe, cluster NICs, rack power, and cooling should be designed together. In a multi-node deployment, the switching and storage fabric can be just as important as the server itself.
Configuration Guidance
Important configuration decisions include:
- CPU choices include Intel Xeon Gold 5415+ (8C/16T, 2.9 GHz), Intel Xeon Gold 5416S (16C/32T, 2.0 GHz), Intel Xeon Gold 6430 (32C/64T, 2.1 GHz)
- Memory can be sized from options such as 128GB DDR5-5600 ECC REG, 16GB DDR5-5600 ECC REG, 16GB DDR5-6400 ECC REG
- Storage can be configured with 1TB NVMe M.2 SSD, 2TB NVMe M.2 SSD, 4TB NVMe M.2 SSD
- Networking options include high-speed Ethernet and InfiniBand adapters for cluster or storage traffic
- For multi-node deployments, plan the NICs, switches, storage, rack power, and cooling together rather than as separate line items
- confirm facility liquid-cooling readiness, service procedure, coolant loop responsibility, and fallback airflow expectations
- size networking, local NVMe, storage fabric, rack power, and cooling around accelerator utilisation rather than GPU count alone
- confirm GPU length, slot spacing, riser layout, host lanes, NIC placement, and PSU headroom before finalising the build
For GPU-heavy deployments, pay close attention to rack power, airflow, service access, high-speed networking, and whether the node will run alone or as part of a cluster. GPUMachines can review the final configuration during quoting, but buyers should still define the intended workload, data sources, model size, user count, storage pattern, and network environment before selecting components.
Recommended Configuration Paths
- Best for AI training: prioritise the full GPU platform, balanced CPU selection, high-capacity memory, local NVMe for datasets and checkpoints, and high-speed networking for multi-node growth.
- Best for inference hosting: focus on GPU memory, network throughput, storage for model repositories, management separation, and enough host memory for concurrent services.
- Best for research or HPC: size CPU, memory, local scratch, and NICs around the mix of simulation, data preparation, and accelerator workloads.
- Best for hosted deployment: review rack power, cooling, remote management, and network design with GPUMachines before purchase.
Alternatives and Related Systems
Buyers should also compare the HGX server range with PCIe GPU servers if their workloads do not need NVLink/NVSwitch. For smaller local development, a tower GPU workstation may be more practical. For teams without suitable rack power or cooling, GPUMachines can also discuss hosted deployment and Buy & Host options.
Buying Through GPUMachines
The fastest next step is to use the G4L3-SD1-LAX5 configurator and select the CPU, RAM, storage, GPU, and networking options that match your workload. GPUMachines can then review the build for compatibility, thermals, power draw, lead time, and cluster fit.
For teams without suitable data centre space, GPUMachines can also discuss Buy & Host, leasing, and GPU Cloud alternatives. That is especially useful when the server needs high-density power, managed networking, or a private hosted environment.
FAQ
Is G4L3-SD1-LAX5 better for training or inference?
It is suitable for training and fine-tuning when the model and software stack can benefit from dense multi-GPU communication. It can also be specified for inference, but the final choice should be based on model size, concurrency, and networking.
How much RAM should I configure?
RAM is configuration-dependent. Match memory capacity to CPU count, dataset preparation, model serving processes, virtualisation needs, and whether the system will run storage or orchestration services alongside GPU workloads.
Does this system need InfiniBand or 400GbE?
High-speed networking depends on deployment design. Single-node systems may only need fast Ethernet, while multi-node training, shared storage, and hosted GPU environments often justify 100GbE, 200GbE, 400GbE, InfiniBand, or separate management networks.
Is this overkill for small AI workloads?
It can be. If the workload is a small inference endpoint, proof-of-concept project, or one-GPU development task, a smaller workstation, hosted GPU option, or lower-density server may be more practical.
Can GPUMachines host this system?
GPUMachines can discuss hosted deployment, leasing, and Buy & Host options where appropriate. This is especially useful when rack power, cooling, remote access, or data-centre operations are concerns.
What should I check before deploying it in a data centre?
Review rack depth, power feeds, cooling, service access, networking, management separation, storage integration, and whether the system needs to operate alone or as part of a cluster.
Verdict
The G4L3-SD1-LAX5 is a strong fit when you want a configurable HGX AI server that can be matched to a real AI, HPC, rendering, storage, or infrastructure workload. Its value is not only in the headline component list, but in how those components are selected and integrated.
Choose it when your team needs a serious infrastructure node with expert configuration support and a clear path to on-premise, hosted, or cluster deployment.
Configure it here: G4L3-SD1-LAX5 on GPUMachines.