Significant price hikes on 5090, L40S and Enerperise Blackwell Series GPUs continues into Q1 2026. Please note Credit Card payments will only work if USD or AED currency is selected on top right corner of the website. For US customers; before placing an order for any crypto miners, inquire with a live chat sales rep or toll-free phone agent about any potential tariffs. HGX B200 lead times are now between 8-20 weeks for Golden Sku selections, with custom BOMs exceed 26 weeks. HGX H200 offerings in stock, as well as limited HGX B300. We are now certified partners of Supermicro in both NA and MENA regions.
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✔ Form Factor: H200 SXM1
✔ FP64: 34 TFLOPS
✔ FP64 Tensor Core: 67 TFLOPS
✔ FP32: 67 TFLOPS
✔ TF32 Tensor Core: 989 TFLOPS
✔ BFLOAT16 Tensor Core: 1,979 TFLOPS
✔ FP16 Tensor Core: 1,979 TFLOPS
✔ FP8 Tensor Core: 3,958 TFLOPS
✔ INT8 Tensor Core: 3,958 TFLOPS
✔ GPU Memory: 141GB
✔ GPU Memory Bandwidth: 4.8TB/s
✔ Decoders: 7 NVDEC, 7 JPEG
✔ Max Thermal Design Power (TDP): Up to 700W each card (configurable)
✔ Multi-Instance GPUs: Up to 7 MIGs @16.5GB each
✔ Interconnect: NVIDIA NVLink®: > 900GB/s, PCIe Gen5: 128GB/s
✔ Server Options: NVIDIA HGX™ H200 partner and NVIDIA-Certified Systems™ with 4 or 8 GPUs, NVIDIA AI Enterprise Add-on
✔ Cooling: Liquid Closed Loop with Thermal Heatsinks
✔ Warranty: 3 years return-to-base repair or replace
Expected delivery in late December, 2024. All sales final. No returns or cancellations. For bulk inquiries, consult a live chat agent or call our toll-free number.
The NVIDIA H200 Tensor Core GPU supercharges generative AI and high-performance computing (HPC) workloads with game changing performance and memory capabilities.
Based on the NVIDIA Hopper™ architecture, the NVIDIA H200 is the first GPU to offer 141 gigabytes (GB) of HBM3e memory at 4.8 terabytes per second (TB/s)—that’s nearly double the capacity of the NVIDIA H100 Tensor Core GPU with 1.4X more memor bandwidth. The H200’s larger and faster memory accelerates generative AI and large language models, while advancing scientific computing for HPC workloads with better energy efficiency and lower total cost of ownership.

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