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Open for bids Tender South Korea NCB TOT Ref. 152434101

Gpu Workstation for Deep Learning Tender in South Korea - 152434101

Tender notice from South Korea, published on 13 Nov 2026.

Exeutive Summary

The NATIONAL GEUMOH INSTITUTE OF TECHNOLOGY INDUSTRY-ACADEMIC COOPERATION FOUNDATION has issued a Tender notice for the procurement of a Gpu Workstation for Deep Learning in the South Korea. This Tender notice was published on 13 Nov 2026 and is scheduled to close on 16 Nov 2026, with an estimated Tender value of KRW 26700000. Interested bidders can access detailed Tender information, eligibility criteria, and complete bidding documents by referencing TOT Ref No. 152434101 and Register on the platform.

Key facts

Summary
Gpu Workstation for Deep Learning
TOT Ref. No.
152434101
Document Ref. No.
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Country
South Korea
Notice type
Tender
Competition
NCB
Publication date
13 Nov 2026
Bid deadline
16 Nov 2026
EMD
Refer Document
Tender value
KRW 26700000
Purchaser ownership
Public
Financier
Self Financed

Purchaser details

Purchaser contact is for registered users

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Tender details

Tender details are for registered users

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Documents

Tender Notice
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Frequently asked questions

The last date to submit a bid for Gpu Workstation for Deep Learning is 16 Nov 2026. Submit before this date to be considered.

This tender has been issued by NATIONAL GEUMOH INSTITUTE OF TECHNOLOGY INDUSTRY-ACADEMIC COOPERATION FOUNDATION in South Korea.

The estimated tender value is KRW 26700000. EMD / bid security details are provided in the tender document.

This tender is Self Financed

The tender notice and related documents are listed in the Documents section above. Select a document to view or download it. Please log in or register to access the documents. If you cannot find additional documents in the Documents section, please log in to your account and Send Document Request from your account section.

The TendersOnTime reference is 152434101. The purchaser document reference is available after login.

Browse other tenders from South Korea or search by relevant keywords and CPV classification.