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NVIDIA Hopper Architecture · GH100

NVIDIA H100 PCIe 80GB
80 GB HBM2e · 3,341 FP8 TOPS

The standard AI training and inference GPU — NVIDIA H100 PCIe delivers 80GB HBM2e, 2 TB/s memory bandwidth, and FP8 Transformer Engine with 3,341 TOPS. The most widely-deployed data-centre AI accelerator, available as a PCIe add-in card for standard server platforms.

All specifications from NVIDIA official datasheet DS-10167-001. No pricing displayed — contact Servnet for UK availability.

NVIDIA H100 PCIe 80GB GPU accelerator card

NVIDIA H100 PCIe 80GB. Photo: 极客湾Geekerwan (CC BY 3.0). Product shown for illustrative purposes.

80 GB
HBM2e VRAM
2 TB/s
Memory bandwidth
3,341
TOPS FP8 (sparsity)
14,592
CUDA Cores
7× MIG
Instances at 10GB
PCIe 5.0
x16 host interface
Technical Specifications

NVIDIA H100 PCIe 80GB — Full Datasheet Specifications

All figures from NVIDIA H100 PCIe datasheet (DS-10167-001). Tensor Core TFLOPS with sparsity assumes 2:4 structured sparse format. Peak rates based on GPU Boost Clock.

Architecture
GPU ArchitectureNVIDIA Hopper (GH100)
CUDA Cores14,592
Tensor Cores456 (4th-generation)
RT Cores114 (3rd-generation)
Process NodeTSMC 4N
Transistors80 billion
Memory
VRAM80 GB HBM2e
Memory Bandwidth2 TB/s (2,000 GB/s)
Memory Bus Width5,120-bit
ECCYes — full memory ECC
Compute Performance
FP64 Tensor Core30.2 TFLOPS
FP32 (CUDA)51.2 TFLOPS
TF32 Tensor Core835.6 TFLOPS (with sparsity)
FP16 Tensor Core1,671 TFLOPS (with sparsity)
BF16 Tensor Core1,671 TFLOPS (with sparsity)
FP8 Tensor Core3,341 TOPS (with sparsity)
INT8 Tensor Core3,341 TOPS (with sparsity)
Connectivity
Form FactorPCIe — dual-slot, FHFL (full-height, full-length)
Host InterfacePCIe 5.0 x16 — 128 GB/s bidirectional
NVLink600 GB/s — NVLink bridge for H100 NVL pair (2-card)
Display OutputsNone (data-centre headless)
Platform
Multi-Instance GPUYes — up to 7 MIG instances at 10GB HBM2e each
Transformer EngineYes — automatic FP8/FP16 precision switching
NVIDIA AI EnterpriseSupported (CUDA, TensorRT, NeMo, Triton)
TDP350W (configurable 310W–350W)
CoolingPassive (requires system-level airflow)

Source: NVIDIA H100 PCIe Datasheet DS-10167-001. All trademarks are the property of NVIDIA Corporation.

PCIe GPU Comparison

H100 vs H200 vs A100 PCIe

GPU ModelVRAMBandwidthFP8 TOPSPCIe GenTDP
THIS PAGEH100 PCIe
80GB HBM2e2.0 TB/s3,341 TOPS5.0350W
H200 NVL PCIe
141GB HBM3e4.8 TB/s3,958 TOPS5.0350W
A100 PCIe
80GB HBM2e1.9 TB/sN/A4.0300W

Source: NVIDIA datasheets DS-10167-001 (H100 PCIe), DS-10581-001_v01 (H200 NVL PCIe), DS-10010-001 (A100 PCIe).

Applications

H100 PCIe Key Workloads

The H100 PCIe is the first data-centre GPU to feature the Transformer Engine — purpose-built for the generation of AI models that underpins GPT, BERT, diffusion, and multimodal architectures.

🧠

Large Language Model Training

FP8 Transformer Engine with automatic mixed precision delivers up to 30x higher LLM training throughput versus A100 on BERT, GPT-style, and diffusion models. 80GB VRAM enables training models up to 30B parameters on a single card.

AI Inference Serving

The H100 PCIe is the reference GPU for production LLM inference with frameworks such as TensorRT-LLM, vLLM, and NVIDIA Triton. FP8 precision doubles throughput vs FP16 for inference at equivalent perplexity.

🔀

Multi-Tenant GPU Partitioning

MIG partitions one H100 PCIe into up to 7 isolated 10GB GPU instances — enabling guaranteed QoS for multiple concurrent workloads, tenants, or microservices on a single physical card.

🔬

Scientific & HPC Simulation

Third-generation NVLink (600 GB/s bridge between paired H100 NVL) and PCIe 5.0 x16 reduce inter-GPU and CPU-GPU data movement bottlenecks in distributed simulation, climate modelling, and molecular dynamics.

🎨

Generative AI (Image & Video)

Stable Diffusion XL, Sora-class video generation, and image-to-3D models benefit from the H100 Transformer Engine and 2 TB/s bandwidth during the denoising attention computation passes.

🔍

RAG & Vector Search at Scale

NVIDIA cuVS and FAISS-GPU leverage H100 HBM bandwidth for billion-scale approximate nearest-neighbour (ANN) vector search — critical for production retrieval-augmented generation (RAG) pipelines.

Compare & Related Products

Need NVIDIA H100 PCIe for AI or HPC?

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