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High Performance Computing (Hpc) Gpu Server Tender - 144564080

The UNIVERSITY OF CALGARY has issued a Tender notice for the procurement of a High Performance Computing (Hpc) Gpu Server in the Canada. This Tender notice was published on 04 Jul 2026 and is scheduled to close on 31 Jul 2026, with an estimated Tender value of Refer Document. Interested bidders can access detailed Tender information, eligibility criteria, and complete bidding documents by referencing TOT Ref No. 144564080, while the tender notice number is 2026RFP0028 and Registering on the platform.

Procurement Summary

Country: Canada

Summary: High Performance Computing (Hpc) Gpu Server

Deadline: 31 Jul 2026

Posting Date: 04 Jul 2026

Other Information

Notice Type: Tender

TOT Ref.No.: 144564080

Document Ref. No.: 2026RFP0028

Competition: ICB

Financier: Self Financed

Purchaser Ownership: Public

Tender Value: Refer Document

Purchaser's Detail

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Tender Details

The University of Calgary is looking for a qualified supplier that can provide High Performance Computing Graphics Processing Unit (GPU) based Servers that meet the specification noted in the RFP.This Request for Proposal is for an addition of GPU based servers to an existing HPC cluster housed at one of the university-s data centers. The main requirement is to enable GPU computing for two types of workloads using CUDA compatible GPUs. The aim is to maximize the total available GPU compute power given the available budget.
Solicitation Type : RFP - Request for Proposal (Formal) Reference Number : 0000326363 Location : Canada, Alberta, Calgary Purchase Type : One Time Only- Delivery Date:2026/09/25 Description : The University of Calgary is looking for a qualified supplier that can provide High Performance Computing Graphics Processing Unit (GPU) based Servers that meet the specification noted in the RFP.This Request for Proposal is for an addition of GPU based servers to an existing HPC cluster housed at one of the university’s data centers. The main requirement is to enable GPU computing for two types of workloads using CUDA compatible GPUs. The aim is to maximize the total available GPU compute power given the available budget.

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