4-GPU vs 8-GPU H100 Server: How to Pick the Right Density

Once you have decided on the H100, the next fork is density: a 4-GPU node or an 8-GPU node. It looks like a simple capacity question, but the two configurations differ in their interconnect topology, their power and cooling demands, and their effective cost per GPU. The right answer depends on how your workloads use the GPUs, not just how many you need.

The interconnect is not the same at each density

On an HGX H100 8-GPU baseboard, four NVSwitch chips provide a fully non-blocking fabric: every GPU reaches every other at the full 900 GB/s NVLink bandwidth. This all-to-all topology is what makes 8-way tensor parallelism efficient. A 4-GPU HGX baseboard also offers full NVLink connectivity across its four GPUs, but you are working within a smaller all-to-all domain. The practical implication: an 8-GPU node keeps a large model's communication on a single high-bandwidth fabric, while two 4-GPU nodes would force that same traffic across a slower node-to-node network.

Power and cooling scale with density

• A 4-GPU SXM5 node draws roughly 2.8 kW from the GPUs alone (4 x 700W), plus CPUs, memory, and networking — typically a 4-5 kW system.
• An 8-GPU SXM5 node draws roughly 5.6 kW from GPUs alone and commonly lands at 10-10.5 kW for the full system.
• 8-GPU HGX chassis are dense 4U-8U systems that often expect high-static-pressure airflow or direct liquid cooling.
• Confirm your rack can deliver the power (often dual high-amperage PDUs) and remove the heat before you commit to 8-GPU density.

Cost-per-GPU and platform overhead

The non-GPU parts of a node — chassis, CPUs, system memory, NICs, power supplies — are a fixed overhead. Spreading that overhead across 8 GPUs instead of 4 lowers the cost and the data-center footprint per GPU. For teams that know they need eight or more H100s, a single 8-GPU node is usually more efficient than two 4-GPU nodes, and it keeps all eight GPUs on one NVSwitch fabric.

When the 4-GPU node is the better fit

• Your largest training or fine-tuning jobs fit comfortably within four GPUs.
• Your facility cannot supply ~10 kW and the cooling for a single dense chassis.
• You want to start a shared team server and grow node count later rather than buying maximum density up front.
• You are running several independent medium workloads that do not need an 8-way fabric.

When to go straight to 8-GPU

If you are training large models, running 8-way tensor parallelism, or planning to scale to multiple nodes with InfiniBand, the 8-GPU HGX node is the standard building block. It gives you the full non-blocking NVSwitch fabric and the best cost-per-GPU, and it is the unit most multi-node reference architectures are built around.

Nexus Compute builds and tests both 4-GPU and 8-GPU HGX H100 servers as turnkey systems — sized to your workload and validated against your rack's real power and cooling limits — with a complete quote back to you within 48 business hours.