NVIDIA H100 80GB PCIe Tensor Core GPU

Brand: NVIDIA | Category: GPUs

SKU: 900-21010-0020-000 | Part #: 900-21010-0020-000 | MPN: 900-21010-0020-000

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PCIe 5.0 x16 support and a 350–500W envelope are the first two specs any infrastructure team checks when evaluating the NVIDIA H100 80GB PCIe Tensor Core GPU (part number 900-21010-0020-000). Those constraints determine whether this card fits your existing server architecture or demands a platform refresh. If your data centre or AI lab is running fourth-generation or newer Intel Xeon Scalable systems, or equivalent AMD EPYC infrastructure, you have the thermal headroom and electrical delivery to deploy this accelerator. Older platforms running PCIe 4.0 or first-generation power supplies will not support this card, so that compatibility check is the gateway decision.

The H100 carries NVIDIA's Hopper architecture with 18,176 Tensor cores across 80 GB of on-card memory connected via a 3.35 TB/s bandwidth path. Those raw numbers matter less than what they do in practice. The card delivers 1,456 TFLOPS in TF32 precision and 2,912 TFLOPS in FP8, which is why large language model inference and fine-tuning workloads favour this GPU. Your AI infrastructure team will appreciate that TF32 sits between FP32 (67 TFLOPS peak) and FP8, giving you algorithmic flexibility without forcing a choice between precision and throughput. In production LLM serving, that flexibility becomes the difference between batching four requests per second or forty.

Memory bandwidth of 3.35 TB/s speaks directly to data-centre architects planning multi-card clusters. A single H100 moves data to and from its memory faster than most PCIe architectures can deliver it, which means you often need multiple cards in the same server or distributed across your fabric to keep any single GPU fed. The 80 GB capacity handles large models that would overflow smaller accelerators, but in production deployments you rarely see a single H100 running alone. Instead, the 900-21010-0020-000 part number appears in clusters of four or eight cards, with each drawing up to 500W under sustained tensor load. Your sysadmin or power operations team needs to know that upfront: eight of these cards in a two-socket server requires dedicated PDU planning.

Operating temperature range of 0–55°C reflects server-class design assumptions. This is not a card you install in a workstation and hope the ambient AC keeps cool. Modern data-centre cooling strategies—hot aisle containment, direct-to-chip liquid loops, or hybrid air-liquid systems—are routine when deploying H100 infrastructure. NVIDIA supplies the card with a 3-year manufacturer warranty, and extended warranty options exist for environments where downtime costs exceed the premium. The full-height, full-length PCIe form factor means you cannot cram other tall cards into adjacent slots, a consideration that matters when planning mixed-workload servers running both storage and compute acceleration.

Omnixon Global stocks the NVIDIA H100 80GB PCIe model (part number 900-21010-0020-000) with regional distribution throughout the GCC, Asia, and Europe. Our team understands the infrastructure decisions behind these deployments—whether your facility is provisioning a dedicated AI cluster, integrating accelerators into existing HPC environments, or building a multi-tenant inference platform. We handle compatibility verification, installation support, and warranty administration for large orders. To request a detailed quote, coordinate a test deployment, or discuss platform integration for your data centre or enterprise, submit an RFQ through our sales portal or contact our technical sales desk directly.

Performance Specifications and Memory Configuration

• Peak Tensor Performance (TF32): 1,456 TFLOPS; FP8: 2,912 TFLOPS for inference workloads requiring lower precision.
• Memory Bandwidth: 3.35 TB/s with 80 GB capacity on a single card, supporting large language models and complex multi-batch operations.
• Peak FP32 Throughput: 67 TFLOPS for traditional single-precision compute (limited by Hopper tensor architecture bias toward lower-precision workloads).
• 18,176 Tensor cores across the Hopper architecture, enabling massively parallel tensor operations in LLM serving, training, and inference.
• PCIe 5.0 x16 Interface: Full 16-lane connectivity for modern server platforms, backward compatible with PCIe 4.0 slots at reduced bandwidth.
• Power Delivery: 12V-2x6 and 16-pin server-class connectors supporting 350–500W peak draw under sustained tensor load.
• Operating Range: 0–55°C, rated for data-centre environments with active thermal management and hot-aisle containment.