Price for 6000 Blackwell series cards have stabilized after 3 consecutive 30% baseline hikes by Nvidia in 2026 and are expected to maintain through the rest of 2026. Supply remains strained. Please note that compliance is mandatory on all AI enterprise compute GPUs and Servers and end-user forms must be filled out before we can share any quotes. Please note Credit Card payments will only work if USD or AED currency is selected on top right corner of the website. HGX B200/B300 lead times are now between 8-14 weeks for Golden Sku, with custom BOMs exceed 20 weeks. For DRAM and SSD bulk orders, please inquire in the chat.
✔ 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.
Verified reviews from real customers on Trustpilot
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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