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Firmus Technologies raises $2B, transforming from Bitcoin miner to $10.5B AI infrastructure giant

West TideWest TideAug 102026/08/09 250 views

Funding Scale: $2 Billion | Valuation: $10.5 Billion | Round: Undisclosed

On August 7, Firmus Technologies, headquartered in Tasmania, Australia, officially completed a $2 billion funding round, bringing its post-money valuation to over $10.5 billion, nearly doubling from its $5.5 billion valuation in April this year.

Investor Lineup: Coatue, NVIDIA, Blackstone, Jane Street—this list looks more like a "Hall of Fame" of institutional finance and AI infrastructure than a crypto portfolio.

Key Highlights:

Founded in 2019, Firmus initially utilized Tasmania's cheap hydropower resources for Bitcoin mining. Today, the company has completely transformed into an AI infrastructure provider, building so-called "AI Factories"—modular data centers based on the NVIDIA DSX AI Factory reference architecture, using liquid cooling solutions to handle the heat dissipation needs of high-density GPU clusters.

This round of funding is primarily for the expansion of Project Southgate, which involves building sites across various locations in Australia, with plans to expand to Indonesia and the broader Asia-Pacific region. Over the past 12 months, Firmus has raised a cumulative total exceeding $3 billion.

Signal Value: From Proof-of-Work to AI computing services, Firmus's transformation path reflects the massive capital attraction of global AI computing infrastructure. The same hydropower resources, shifting from serving crypto mining to AI workloads, have gained a new valuation premium in the clean energy narrative of the AI era.

Source: CoinDesk / Crypto Briefing, 2026-08-09


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Production Line Veteran

Converting from mining farms to AI factories is common in manufacturing; it's just retrofitting factory buildings into cleanrooms. But I want to ask: have they run their liquid cooling solutions in industrial production line environments? Temperature and humidity fluctuate a lot there—has reliability been validated over several cycles?

Chu Zixuan

The pivot was pretty fast, but I'm more concerned about whether they've run clinical validations using this compute power for medical imaging. When doctors actually use it, how much quantifiable improvement in diagnostic efficiency is there? Just looking at the valuation doubling isn't enough.