PANews reported on October 28th that according to The Block, TeraWulf has partnered with AI cloud platform Fluidstack to build a 168MW high-performance AI data center in Texas. The project is backed by a $1.3 billion lease from Google. TeraWulf holds a 51% stake in the project, and the total contract value is $9.5 billion. The project is expected to be completed in the second half of 2026, pushing the company's total capacity to over 510MW. The company also announced an 84% year-over-year increase in Q3 revenue. The news sent its stock price surging 25%, bringing its year-to-date gains to over 130%.PANews reported on October 28th that according to The Block, TeraWulf has partnered with AI cloud platform Fluidstack to build a 168MW high-performance AI data center in Texas. The project is backed by a $1.3 billion lease from Google. TeraWulf holds a 51% stake in the project, and the total contract value is $9.5 billion. The project is expected to be completed in the second half of 2026, pushing the company's total capacity to over 510MW. The company also announced an 84% year-over-year increase in Q3 revenue. The news sent its stock price surging 25%, bringing its year-to-date gains to over 130%.
TeraWulf partners with Fluidstack to launch $9.5 billion AI data center project, backed by Google; stock price surges 25%
2025/10/28 22:27
PANews reported on October 28th that according to The Block, TeraWulf has partnered with AI cloud platform Fluidstack to build a 168MW high-performance AI data center in Texas. The project is backed by a $1.3 billion lease from Google. TeraWulf holds a 51% stake in the project, and the total contract value is $9.5 billion. The project is expected to be completed in the second half of 2026, pushing the company's total capacity to over 510MW. The company also announced an 84% year-over-year increase in Q3 revenue. The news sent its stock price surging 25%, bringing its year-to-date gains to over 130%.
Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact service@support.mexc.com for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.
Share Insights
You May Also Like
Technical Setup for RECKONING: Inner Loop Gradient Steps, Learning Rates, and Hardware Specification
This article outlines the implementation details for RECKONING, which uses a GPT-2-base model and runs on NVIDIA A100 GPUs.
Share
Hackernoon2025/10/29 23:29
The Strength of Dynamic Encoding: RECKONING Outperforms Zero-Shot GPT-3.5 in Distractor Robustness
This article benchmarks the GPT-3.5 LLM on multi-hop reasoning datasets, finding that RECKONING's performance significantly surpasses both zero-shot and few-shot GPT-3.5 prompting.
Share
Hackernoon2025/10/29 23:57
ICO Airdrops Explained in 2025: Guide With Nexchain Case Study
Airdrops have become one of the most effective ways for new blockchain projects to grow their communities. By distributing free tokens, teams reward early supporters and attract long-term users. For The post ICO Airdrops Explained in 2025: Guide With Nexchain Case Study appeared first on CryptoNinjas.
Share
Crypto Ninjas2025/09/19 03:19