SpaceX Seeks $40 Billion in Debt to Finance Nvidia Chip Purchases
publishers 30articles 36first reported 6 Oct, 22:28 UTCdeveloping since 7 Oct · 10 editions
SpaceX is in early talks with banks and investors to raise as much as $40 billion to finance purchases of Nvidia artificial-intelligence chips, according to reports by the Financial Times and CNBC.
The proposed package could include about $10 billion in bank loans and $30 billion in investment-grade debt. Apollo Global Management and a consortium of banks are discussing the financing, with Apollo expected to play a leading role. Pimco is also reportedly among a limited group of potential participants.
Discussions remain preliminary, and there is no guarantee a deal will be completed. CNBC reported that no closing timeline had been set, while the Financial Times report cited by Tom’s Hardware said the transaction could be completed in 2027. The parties named in the reports did not comment.
The financing would support an expansion of computing capacity using Nvidia hardware. Tom’s Hardware estimated that $40 billion could buy about 5,000 Vera Rubin NVL72 systems containing roughly 360,000 accelerators, assuming a price of approximately $8 million per system. That calculation is an estimate rather than a disclosed purchasing plan.
CNBC reported that SpaceX raised $25 billion in a June debt sale to support its AI operations, including computing capacity rented to customers such as Anthropic and Google.
HOW THIS STORY WAS MADE
Written from 8 articles, 2 with the publisher's own text; 8 independent newsrooms once syndicated copies count as one; this version written 36 h after the record first saw the story; the editor kept it as written.
- Publishers
- 30
- Source articles
- 36
- Given to the writer
- 8, 2 with the article's own text
- Independent newsrooms
- 8
- This version written
- 36 h after the record first saw the story
- Second model (editor)
- kept as written
- Publication gate
- passed
- Human review
- none
WHAT THE EDITOR DID
The second model checked the writer's draft against the same sources and published it as written.
Written by a language model from the sources above, then checked by a second model that may only cut, attribute or correct. How it works →