Nvidia gpu chip
I have found results from400+products and100+suppliers aboutNvidia gpu chip
Nvidia Hopper Gh100 GPU Unveiled: Gh100-885K-A1 4nm Data Center Chip, up to 4000 Tflops Compute, Hbm3 3 Tb/S Memory
Nvidia Graphic Chip of Nvidia A100 80GB900-21001-0020-100 H100- 80GB Pcie
Supermicro GPU Superserver Sys-422ga-Nbrt-Lcc Dp Intel 4u Liquid-Cooled System with Nvidia Hgx B200 8-GPU Server
Nvidia H100 Tensor Core GPU Sxm5/Pcie — High Performance Ai Accelerator
Nvidia Tesla Rtx A4000 16gbrtx A5000 24GB Nvidia Rtx A6000 48GB GPU Pcie 4 X16 Gddr6 Professional Card
Nvidia Tesla Nvl 141GB GPU H800 80GB Professional Computing Chatgpt Ai Graphic Card Graphics Card for Workstation or Server
Nvidia Tesla A800 96GB/80GB/40GB Sxm/Pcie Ai Deep Learning Advanced Card Server Data Computational Processing Rtx A6000 GPU Module
New Original GPU for Nvidia Hopper H100 Nvl 80GB Graphic Card
New Original GPU for Nvidia AMP A100 Nvl 80GB Graphic Card
New Original GPU for Nvidia Hopper H100 Nvl 96GB Graphic Card
Nvidia H100 94GB Pcie Data Center GPU Accelerator
Advanced Deep Learning Nvidia Tesla L40s GPU Deep Learning Computing Edge Computing Ai Server Graphics Card
Nvidia L40s 48GB Pcie Gen4 Gddr6 GPU Graphic Cards for Ai Server Deep Learning and Hpc
High-Performance Nvidia Tesla A30 Tensor Core GPU Accelerator Gddr6 Graphics Card for Deep Learning
Original Nvidia H200 Nvl 141GB Hbm3e Pcie Data Center Ai Accelerator GPU
Nvidia H100 80g Pcie GPU Accelerator Gh100 884 Chip Ai Computing Card
Original Nvidia A100 Pcie GPU Card, Deep Learning Accelerator for Data Center
GPU a+ Server as -A126GS-Tnbr Dp AMD 10u System with Nvidia Hgx B200 8-GPU
Dual-Node Nvidia Gh200 Grace Hopper GPU for 1u Servers
Supermicro GPU Ars-111gl-Nhr Nvidia Gh200 Grace Hopper Superchip
GPU Ars-121L-Dnr New Nvidia Bluefield-3 or Nvidia Connectx-7
GPU a+ Server as -4126GS-NBR-Lcc Dp AMD 4u Liquid-Cooled System with Nvidia Hgx B200 8-GPU
GPU Ars-111gl-Nhr-Lcc Nvidia H200 Grace Hopper Superchip
High-Performance Nvidia Tesla Graphic Card A100 80GB Customized Version GPU for Ai Computing
Sourcing guidance for Nvidia Gpu Chip
What are the key technical specifications to verify when sourcing Nvidia GPU chips for AI or data center applications?
When sourcing high-end GPUs like the H100, A100, or Blackwell series, you must prioritize Architecture (e.g., Hopper, Ampere), CUDA Core count, and Tensor Core generations. For data center use, verify the HBM (High Bandwidth Memory) capacity and bandwidth (e.g., HBM3 or HBM3e), as these directly impact LLM training speeds. Additionally, confirm the TDP (Thermal Design Power) to ensure your cooling infrastructure can handle the heat dissipation requirements, and check for NVLink support to facilitate multi-GPU scaling.
How can buyers ensure the authenticity and quality of Nvidia GPU chips in the secondary or distribution market?
To avoid counterfeit or refurbished units, buyers should request the Original Manufacturer Certificate (COC) and verify the Date Code (D/C) to ensure the chips are from a recent production batch. It is critical to conduct Visual Inspection (marking, alignment, and packaging) and, if possible, X-ray testing to check for internal bonding integrity. Always prioritize suppliers on Made-in-China.com who offer third-party lab testing reports (such as White Horse or CECC testing) to validate electrical parameters against the official datasheet.
What compliance standards and certifications are mandatory for importing GPU chips?
GPU chips must comply with international environmental and safety standards. Ensure the products are RoHS (Restriction of Hazardous Substances) compliant and REACH certified. For finished graphics cards or server modules, CE (Europe), FCC (USA), and VCCI (Japan) certifications are necessary. Furthermore, because high-performance GPUs are often subject to Export Control Classifications (ECCN), buyers must ensure the specific model is cleared for export to their region to avoid legal complications.
Cross-Border Procurement Risks and Strategic Advice for Nvidia GPUs
What are the primary risks associated with international trade of high-performance GPU chips?
The most significant risks include Export Control Restrictions and Price Volatility. High-end AI chips are frequently subject to changing trade policies (such as U.S. Department of Commerce regulations), which can lead to shipment seizures or order cancellations. Additionally, the market price for GPUs fluctuates daily based on supply chain shortages; therefore, it is advised to lock in prices with a formal Proforma Invoice (PI) and use Escrow payment services provided by platforms like Made-in-China.com to protect your funds until delivery is confirmed.
How should buyers negotiate with suppliers to ensure long-term supply stability?
Negotiations should focus on Lead Time Guarantees and Buffer Stock Agreements. Instead of just haggling on unit price, ask the supplier for a rolling forecast agreement where they reserve a specific capacity for you. For bulk orders, negotiate a staggered payment schedule (e.g., 30% deposit, 70% after third-party inspection) to maintain leverage. Always clarify the Warranty and RMA (Return Merchandise Authorization) process, ensuring the supplier covers shipping costs for defective units found during the initial QC phase.
What are the best practices for shipping and logistics for sensitive electronic components like GPU chips?
GPU chips are highly sensitive to Static Electricity (ESD) and Physical Impact. Ensure the supplier uses Anti-Static Bags (ESD shielding) and Vacuum Packaging with humidity indicator cards. For international shipping, use Insured Air Freight rather than sea freight to minimize exposure to moisture and prolonged vibration. Request double-boxing with shock-absorbent foam and ensure the outer carton is marked with 'Fragile' and 'Sensitive Electronics' labels to prevent mishandling during customs inspections.





























