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NVIDIA H100 SXM vs NVIDIA H800 (2023): Spec Comparison & Buyer's Guide

In AI infrastructure selection, NVIDIA H100 SXM and NVIDIA H800 (2023) are two accelerators frequently compared. This article contrasts them item by item — architecture, compute, memory, power, and release cadence — to help you quickly judge which fits training or inference workloads.

Spec Comparison Table​

VendorNVIDIA H100 SXMNVIDIA H800 (2023)
VendorNVIDIANVIDIA
ArchitectureHopper GH100Hopper GH100
ProcessTSMC 4NTSMC 4N (4nm)
Release Date2022 3 GTC2023
FP8 Compute3,958 TFLOPS3,958 TFLOPS
FP16 Compute—1,979 TFLOPS
FP32 Compute67 TFLOPS67 TFLOPS
INT8 Compute—3,958 TOPS
Memory Type——
Memory Capacity80 GB HBM3—
Memory Bandwidth3.35 TB/s3.35 TB/s
TDP Power700 W350W

Key Differences​

  • Power: NVIDIA H800 (2023) has a TDP of 350W, lower than NVIDIA H100 SXM's 700 W, friendlier to datacenter PUE and cooling.

Selection Advice​

  • When chasing extreme single-card compute and a mature toolchain, prioritize NVIDIA H100 SXM; if budget, power wall, or local support are hard constraints, NVIDIA H800 (2023) often fits better. Use this site's AI Compute Card Comparison Tool to validate multiple chips side-by-side before deciding.

FAQ​

What are the main differences between NVIDIA H100 SXM and NVIDIA H800 (2023)?​

The core difference is architecture and compute density: NVIDIA H100 SXM uses Hopper GH100, FP8 ~3,958 TFLOPS, memory 80 GB HBM3; NVIDIA H800 (2023) uses Hopper GH100, FP8 ~3,958 TFLOPS, memory —. See the comparison table above.

What is the TDP (power) of NVIDIA H100 SXM?​

NVIDIA H100 SXM has a TDP of 700 W; actual whole-system power also includes board, fans, and PUE.

Which is better for large-model training / inference?​

Training values memory capacity, bandwidth, and multi-card interconnect; inference values single-card throughput and power efficiency. Combine the "Key Differences" and "Selection Advice" above with your batch size, model size, and SLA.

How much do NVIDIA H100 SXM and NVIDIA H800 (2023) differ in memory capacity?​

NVIDIA H100 SXM is 80 GB HBM3, NVIDIA H800 (2023) is —; the gap directly affects loadable model size and context length.