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# ByteDance raises $29.6bn, Asia’s second-biggest loan this year, and pays less for it

**[The Next Web](https://daily.dev/sources/tnw)** · 4 min read · 0 upvotes · 0 comments

## Summary

ByteDance has secured a $29.6bn syndicated loan, Asia's second-largest this year after SoftBank's $40bn OpenAI-linked bridge facility, with Citigroup and JPMorgan coordinating a three-year deal extendable to five. The pricing, 68 basis points over SOFR, is lower than the 85 bps it paid in 2024 despite borrowing nearly three times as much, reflecting lender confidence in TikTok and Douyin revenue as collateral. Proceeds are earmarked for AI data centre buildout, with ByteDance weighing up to $70bn a year in capex while working around US export controls on Nvidia chips by using custom Arm/RISC-V silicon and Qualcomm inference parts. The company also pays over $1bn a year to run OpenAI models via Microsoft Azure while building domestic hardware to reduce that dependence.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://thenextweb.com/news/bytedance-29-6-billion-loan-ai-data-centres>

## Questions this post answers

### How much did ByteDance borrow in its latest offshore loan and how does the pricing compare to its 2024 facility?

ByteDance secured a $29.6bn loan, Asia's second-largest this year, at an opening margin of 68 basis points over SOFR. That is down from the 85 basis points it paid on its 2024 offshore facility of $10.8bn, meaning it borrowed roughly three times as much money at a materially lower price, coordinated by Citigroup and JPMorgan over a three-year term extendable to five.

_daily.dev tracks stories like ByteDance's AI infrastructure financing for engineers following the compute buildout race._

### How is ByteDance building AI data centre capacity while facing US export controls on Nvidia chips?

ByteDance is working around restricted access to Nvidia's advanced chips by relying on custom silicon built on Arm and RISC-V architectures, Qualcomm inference parts, and domestic Chinese suppliers. This approach makes assembling the same amount of compute more expensive, part of a capital plan that could reach up to $70bn a year as it expands a data centre cluster in Inner Mongolia and trains a ten-trillion-parameter model.

_engineers weighing chip and infrastructure constraints for AI workloads can follow this buildout on daily.dev._

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---

Tags: [#openai](https://daily.dev/tags/openai), [#nvidia](https://daily.dev/tags/nvidia), [#ai-infrastructure](https://daily.dev/tags/ai-infrastructure)

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