Short answer: H100 is the better-documented choice when you need known compute, memory-bandwidth, interconnect, and power specifications; H20 may offer more memory in documented SXM5 configurations, but the official material cited here does not establish a comparable H20 performance profile. Choose only after matching the exact GPU variant and complete server to your workload—and confirming that H20 can legally and commercially be supplied to your destination.
NVIDIA H100 vs. H20: What the available specifications show
These names do not describe one interchangeable card each. NVIDIA publishes separate H100 SXM and H100 NVL specifications. Its AI Enterprise vGPU documentation identifies H20 SXM5 variants with 96GB and 141GB of memory. Those sources do not provide a complete, directly comparable specification sheet for H20, so they cannot support a reliable numeric H100-versus-H20 performance ratio.
| Specification | H100 SXM | H100 NVL | H20 SXM5 |
|---|---|---|---|
| Memory | 80GB | 94GB | 96GB or 141GB, as listed in NVIDIA AI Enterprise vGPU documentation |
| Memory bandwidth | 3.35TB/s | 3.9TB/s | Not stated in the cited NVIDIA vGPU documentation |
| FP8 Tensor Core rate | 3,958 teraFLOPS, with sparsity | 3,341 teraFLOPS, with sparsity | Not stated in the cited NVIDIA vGPU documentation |
| NVLink bandwidth | 900GB/s | 600GB/s | Not stated in the cited NVIDIA vGPU documentation |
| Configurable power | Up to 700W | 350–400W | Not stated in the cited NVIDIA vGPU documentation |
H100 figures are NVIDIA product-page specifications, not independent benchmark results. The FP8 rates carry NVIDIA’s sparsity qualification. H20 values shown here are memory variants documented for vGPU profiles; they are not a complete list of all possible system-level configurations. Sources: NVIDIA H100 specifications and NVIDIA AI Enterprise Hopper vGPU types.
H100 vs. H20 for AI workloads
Model fit and memory capacity
Memory capacity can determine whether a model, its context, and the workload’s working data fit on a GPU or need to be split across devices. The documented 141GB H20 SXM5 variant has more listed memory than either H100 configuration in the comparison above; H20 is documented in 96GB as well. That capacity alone does not establish that a workload will run faster or more cheaply: performance also depends on compute, memory bandwidth, software, and how the server is configured.
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Compute-bound training and inference
H100 has published Tensor Core rates by form factor and precision, including the FP8 figures above. NVIDIA describes its H100 Transformer Engine as providing “up to 4X faster training” for GPT-3 (175B) models compared with the prior generation. That is NVIDIA’s stated prior-generation comparison—not an H100-versus-H20 result. The cited official H20 documentation provides vGPU profiles rather than a matching compute-rate table, so it does not establish which GPU delivers higher throughput on a particular model.
Multi-GPU scaling
For jobs distributed across accelerators, the GPU-to-GPU link and server topology can matter as much as single-device specifications. NVIDIA lists 900GB/s NVLink for H100 SXM and 600GB/s for H100 NVL. The cited H20 documentation does not establish directly comparable H20 interconnect details. Ask the system vendor for the exact baseboard, GPU count, interconnect topology, and supported collective communication setup rather than inferring H20 scaling from its memory size.
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Power, cooling, and rack fit
NVIDIA lists H100 SXM at up to 700W configurable and H100 NVL at 350–400W configurable. The cited H20 source does not give a comparable power figure. These are accelerator specifications, not a full server power budget; assess the complete system’s electrical supply, cooling, and rack constraints with the supplier.
How to choose a configuration
- Identify the exact form factor. Confirm whether the quote is for H100 SXM, H100 NVL, or H20 SXM5 and record the memory capacity. Do not apply one variant’s bandwidth, power, or interconnect figures to another.
- Check model and workload fit. Estimate the memory needed for model weights, runtime state, context, and batch size. Then establish whether the limiting factor is memory capacity, compute throughput, memory bandwidth, or latency.
- Specify the system, not just the GPU. Request the accelerator count, interconnect topology, CPU and memory configuration, server power envelope, and cooling requirements. These determine whether a multi-GPU configuration can meet the workload’s scaling and operating constraints.
- Ask for workload-specific evidence. Request benchmark results for the intended model, precision, software stack, batch or concurrency level, and number of GPUs. The cited official sources do not establish a fair H100-versus-H20 benchmark.
- Verify delivery eligibility before committing to H20. Confirm the destination, supplier, end customer, and any applicable export license or sales restriction, then obtain written confirmation that the quoted system can be delivered.
H20 availability and export restrictions
Availability is not just a matter of finding a system listing. In its fiscal 2027 second-quarter Form 10-Q, published August 27, 2026, NVIDIA said the U.S. government informed it in April 2025 that a license was required for H20 exports to China, including Hong Kong and Macau, and D:5 countries, as well as to companies headquartered in those places or whose ultimate parent is there. NVIDIA also reported that licenses granted beginning in August 2025 permitted certain shipments, while PRC government restrictions limited sales. This is a dated company disclosure, not a determination that any particular buyer or transaction qualifies; check current rules and supplier eligibility for the intended destination. Read NVIDIA’s Form 10-Q.
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What the evidence does—and does not—support
The cited NVIDIA material supports a variant-specific H100 specification comparison and confirms H20 SXM5 memory variants in NVIDIA vGPU documentation. It does not establish a full current H20 compute, bandwidth, power, and interconnect specification set, or an independent head-to-head benchmark. Treat any purchase comparison that claims a universal performance winner or precise H100-to-H20 ratio as dependent on additional configuration and workload evidence.
Quick Recap
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