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NVIDIA Hopper H100 and H200 systems
The Hopper decision is mostly about memory and form factor. This page keeps SXM and PCIe on separate rows, because they are separate products, and works through the system choices that follow from each.
What are NVIDIA H100 and H200 systems?
NVIDIA H100 and H200 are Hopper-generation data-center GPUs available through multiple system configurations. H200 generally addresses workloads that benefit from greater accelerator memory and memory bandwidth, while the correct system still depends on form factor, interconnect, server design, workload, software, power, cooling, condition, warranty, and verified availability.
Four products, not two
NVIDIA publishes separate specifications for the SXM and PCIe versions of each generation. Treating them as one product is the single most common source of misconfigured Hopper quotes.
| Specification | H200 SXM | H200 NVL | H100 SXM | H100 NVL |
|---|---|---|---|---|
| GPU memory | 141 GB | 141 GB | 80 GB | 94 GB |
| Memory bandwidth | 4.8 TB/s | 4.8 TB/s | 3.35 TB/s | 3.9 TB/s |
| Form factor | SXM | PCIe, dual-slot air-cooled | SXM | PCIe, dual-slot air-cooled |
| Max thermal design power | Up to 700W, configurable | Up to 600W, configurable | Up to 700W, configurable | 350W to 400W, configurable |
| Interconnect | NVLink 900 GB/s, PCIe Gen5 128 GB/s | 2-way or 4-way NVLink bridge at 900 GB/s per GPU, PCIe Gen5 128 GB/s | NVLink 900 GB/s, PCIe Gen5 128 GB/s | NVLink 600 GB/s, PCIe Gen5 128 GB/s |
| FP8 Tensor Core (with sparsity) | 3,958 TFLOPS | 3,341 TFLOPS | 3,958 TFLOPS | 3,341 TFLOPS |
| FP16 Tensor Core (with sparsity) | 1,979 TFLOPS | 1,671 TFLOPS | 1,979 TFLOPS | 1,671 TFLOPS |
| FP64 Tensor Core | 67 TFLOPS | 60 TFLOPS | 67 TFLOPS | 60 TFLOPS |
| Multi-Instance GPU | Up to 7 MIGs at 18 GB each | Up to 7 MIGs at 16.5 GB each | Up to 7 MIGs at 10 GB each | Up to 7 MIGs at 12 GB each |
At matching form factors the compute figures are the same across the two generations. The generational difference is memory capacity and bandwidth, which is why the H100 to H200 decision is usually settled by whether the workload is memory bound.
Which one the workload actually wants
H200 usually wins
Large-model inference, long context, and workloads where the model, activations, or KV cache force sharding on an 80 GB part. The 141 GB capacity and 4.8 TB/s bandwidth are what change the outcome, not the compute figures.
H100 remains competitive
At matching form factors NVIDIA publishes identical Tensor Core throughput. If the workload fits comfortably in 80 GB, the extra memory buys nothing and the decision moves to the total transaction.
SXM for multi-GPU training
Full NVLink bandwidth across an 8-GPU baseboard, with the higher power budget and the cooling design that comes with it.
PCIe for air-cooled racks
NVIDIA describes H200 NVL as suited to lower-power air-cooled enterprise rack designs with flexible configurations. Where the facility limits density, this is often the deployable option.
How Hopper GPUs are packaged into servers
HGX baseboard systems
NVIDIA publishes HGX H100 and HGX H200 partner and NVIDIA-Certified Systems with 4 or 8 GPUs. This is the usual shape for training clusters.
DGX systems
NVIDIA publishes DGX H100 with 8 GPUs as its own complete system, with NVIDIA software and support attached.
MGX and PCIe partner systems
NVIDIA publishes MGX H200 NVL partner and NVIDIA-Certified Systems with up to 8 GPUs, and PCIe partner systems with 1 to 8 GPUs, which suits mixed and inference-oriented estates.
Manufacturer implementations
Dell publishes PowerEdge XE9680 with 8 NVIDIA H100 or H200 GPUs, HPE publishes Cray XD670 as a 5U chassis with 8 NVIDIA H200 Tensor Core SXM5 GPUs and a direct liquid cooling option, and Lenovo publishes ThinkSystem SR675 V3 with NVIDIA H100 and L40S GPUs.
New and qualifying recovered systems
Hopper is the generation where recovered supply is most common, which makes lot-level evidence more important than it is for a new-build purchase.
- Exact GPU model and form factor, SXM or PCIe
- GPU count and node configuration
- CPU, memory, storage, NIC, and interconnect configuration
- Firmware level and software compatibility
- Power draw and connector requirements
- Equipment condition and provenance
- Inspection and test evidence, where it exists
- Warranty provider, scope, term, and transferability
- Availability confirmation with its date and expiration
- Export, end-use, and geographic restrictions
SLYD does not represent that every recovered lot is tested, graded, warrantied, or ready for immediate delivery. Condition, testing, and warranty evidence are disclosed for the specific lot when they exist.
What the deployment needs around it
Rack power design
Configurable TDP means the same GPU can be deployed at different power points. The rack design follows the configuration actually chosen.
CoolingAir or direct liquid
Hopper systems are widely available air-cooled, which is often why they remain deployable in facilities that cannot take current rack-scale platforms.
NetworkCluster fabric
Scale-out fabric, adapters, optics, and topology for connecting nodes into a working cluster.
StorageData path
Capacity and sustained throughput for datasets, checkpoints, and model artifacts.
Hopper server questions
What is the difference between NVIDIA H100 and H200?
Both are Hopper-generation data-center GPUs with the same published Tensor Core throughput at matching form factors. The difference is memory. NVIDIA publishes 141 GB and 4.8 TB/s of memory bandwidth for H200 SXM against 80 GB and 3.35 TB/s for H100 SXM, so H200 addresses workloads limited by accelerator memory or memory bandwidth.
Is H200 only available in an SXM form factor?
No. NVIDIA publishes both H200 SXM and H200 NVL. H200 NVL is a PCIe dual-slot air-cooled part with a configurable TDP of up to 600W, described by NVIDIA as suited to lower-power air-cooled enterprise rack designs, while H200 SXM has a configurable TDP of up to 700W.
What is the difference between SXM and PCIe Hopper GPUs?
They are different products with different published specifications, not packaging variants of the same part. SXM parts sit on an integrated baseboard with higher power budgets and full NVLink bandwidth. PCIe parts install in standard server slots at lower power with reduced interconnect bandwidth. Memory capacity, bandwidth, TDP, and NVLink figures all differ between them.
How many H100 or H200 GPUs go in a server?
NVIDIA publishes HGX H100 and HGX H200 partner and NVIDIA-Certified Systems with 4 or 8 GPUs, DGX H100 with 8 GPUs, and MGX H200 NVL partner and NVIDIA-Certified Systems with up to 8 GPUs. PCIe-based partner systems are published with 1 to 8 GPUs. The actual configuration comes from the specific server model.
Should I still buy Hopper, or move to Blackwell?
It depends on the workload, the facility, and the total transaction. Hopper systems have lower power and cooling requirements than current Blackwell platforms, which matters if the site is constrained, and a mature software ecosystem. Blackwell platforms offer more memory and interconnect bandwidth per system. The comparison should be run on the specific workload rather than by generation.
What should I check when evaluating recovered Hopper systems?
Confirm the exact GPU model and form factor, GPU count and node configuration, CPU, memory, storage and network configuration, firmware level, condition and provenance, any inspection or test evidence, warranty provider and transferability, quantity and location, and the export and end-use restrictions that apply. Evidence is evaluated for the specific lot rather than assumed.
Do NVIDIA's published Hopper performance figures assume sparsity?
The Tensor Core rows do. NVIDIA marks TF32, BFLOAT16, FP16, FP8, and INT8 Tensor Core figures for H100 and H200 as with sparsity, and additionally marks the H200 specification table as preliminary specifications that may be subject to change. Both caveats should travel with the numbers.
Sources for the specifications on this page
- NVIDIA, H200 product page. Reviewed 18 August 2026. nvidia.com/en-us/data-center/h200
- NVIDIA, H100 product page. Reviewed 18 August 2026. nvidia.com/en-us/data-center/h100
- Dell, PowerEdge AI servers catalog. Reviewed 18 August 2026. dell.com PowerEdge AI servers
- HPE, Cray XD670 product page. Reviewed 18 August 2026. hpe.com Cray XD670
- Lenovo Press, AI servers documentation. Reviewed 18 August 2026. lenovopress.lenovo.com/servers/ai-servers
Specifications belong to the manufacturers named and are reproduced with their own caveats. They are not SLYD guarantees of performance, configuration, or availability.
Scope a Hopper requirement
Share the workload, memory requirement, node count, facility constraint, and condition tolerance. Those decide the form factor and the sourcing path.
Page updated: August 18, 2026