The Toronto Region’s AI advantage is built on decades of investment in creating an environment that not only attracts and retains top talent, but also drives innovation.
It’s the physical stack that has scaled in pace with the demands of growth that enables teams to move quickly while pushing the boundaries of AI’s capabilities: a dense hardtech corridor of startups alongside the biggest names in technology today, combined with an expansive dark-fibre backbone and high-performance shared compute capacity that powers research and supports applied outcomes.
Silicon that stays and scales
Consider the region’s rich silicon heritage, to start. One way to see it is through the success of Advanced Micro Devices (AMD), which recently marked its 40th anniversary in Markham, 30 km northeast of downtown Toronto and the largest of nine communities in York Region. The company now has more than 3,000 engineers, about 10 per cent of AMD’s global headcount, focused on virtually every aspect of its diverse, market-leading portfolio. That includes the graphics processing units (GPUs) that power a lot of machine learning and AI training in large-scale data centres and supercomputers, as well as programmable chips for the automotive and robotics sectors.
“Our site in Markham is one of the largest R&D centres for us globally,” says Chris Smith, who leads several groups within AMD’s Canadian headquarters as corporate vice president. “We participate in all aspects of development, from hardware through the various layers of software and platform design.”
The company also operates its own state-of-the-art data centre. “It’s liquid-cooled with giant chillers in the parking lot to make sure we can run the latest and greatest AI systems at peak performance and do all of the at-scale validation of platforms before sending them off to the likes of Google, Meta, and OpenAI,” Smith explains.
His reference to OpenAI is timely. In October 2025, AMD and OpenAI announced a strategic partnership that will further drive advances in silicon.
All that activity has an impact beyond AMD’s own roadmap. “We’ve spun out a few companies in the silicon and hardware space,” Smith explains. “Through former employees or partnerships with local companies, we’re also very much engaged in academic research in the Greater Toronto Area.”
At the same time, the company exerts an outsized gravitational pull. “We’re one of the largest employers of interns and co-op students and one of the largest hirers of new college grads in the GTA and across the country, specifically in engineering and computer science domains,” Smith notes.
Not surprisingly, given its longstanding presence in Markham, AMD’s perspective regarding the region’s benefits is often sought by other companies that are considering locating advanced chip manufacturing and AI engineering there.
“There are lots of strong proof points that this is a great place to build,” Smith says. Those range from a deep pool of experienced chip designers and systems engineers, to steady inflows of co-op talent from nearby universities, to a growing cluster of customers and partners across automotive, telecommunications, and AI.
Our site in Markham is one of the largest R&D centres for us globally.— Chris Smith, Corporate Vice President and Head, Toronto Markham Design Centre, AMD
Over time, those ingredients reinforce each other, making it easier for new hardware companies to land and scale. For the region’s AI economy, that means the hardware underpinning global systems isn’t just shipped in from elsewhere — it’s being imagined, designed, and tested in the same geography as the models and applications that run on top of it. While his message might seem tailored for the C-suite of established global players, Smith also makes a compelling case for those starting on a technology journey or seeking to scale up their small-to-mid-sized operation.
Enabling companies to compete globally
If AMD represents the silicon layer, ventureLAB shows how that capability becomes accessible to emerging companies. Founded in Markham in 2011, ventureLAB provides a springboard for entrepreneurs through access to its state-of-the-art, and Canada’s only, hardware- and semiconductor-focused facilities for prototyping and testing.
It also has innovation spaces for learning, networking, and access to industry leaders with decades of experience, which is invaluable for those innovating in high-growth areas such as AI, medtech, and automobility.
“At any point in time, we work with 100 to 150 companies,” explains Garry Chan, ventureLAB’s Chief AI Advisor. “That gives us a pretty good window into the challenges and opportunities out there. At the same time, we have this high concentration of people and a cross-pollination of interests, so when someone is stuck on something they’re like two or three degrees removed from an answer. That’s really powerful, when you think about it.”
Several programs provide direct support to founders. ventureLAB’s Hardware Catalyst Initiative (HCI), for example, enables silicon and hardware companies to grow and scale locally so they can compete globally. Dedicated advisors help with product design, IP identification, customer engagement, and financial planning. The initiative is funded by provincial and federal government partners.
Critically, the programs aren’t limited to firms within the GTA. As Chan notes, ventureLAB is working with companies from New Brunswick all the way to British Columbia, giving founders across the country access to tools, expertise, and infrastructure that would otherwise be out of reach for early-stage teams.
In a region that now spans the full stack of AI — from chips and optics to models and applications — ventureLAB is what keeps that stack open and porous. It lowers the barrier for new hardware and AI companies to plug into the same calibre of tools, mentors, and partners that global players enjoy. Instead of infrastructure sitting behind the walls of a few large firms, it becomes shared scaffolding the whole cluster can build on, which is ultimately what turns a collection of companies into a true AI powerhouse.
Taking connectivity to a new level
While ventureLAB accelerates hardware builders, YorkNet provides the network capacity that AI workloads depend on.
“AI needs bandwidth. There’s no way it’s going to happen without supreme connectivity and putting in dark fibre that can then be lit with the right equipment,” says Jonathan Wheatle, Director of Economic Strategy with the Regional Municipality of York.
YorkNet is a municipally owned corporation delivering government-funded infrastructure to connect communities with reliable, high-speed, low-latency internet. Dark fibre is what enables future connectivity, exactly what a growing ecosystem of AI companies will need.
In 2024 alone, YorkNet constructed 296 km of conduit infrastructure for dark fibre — the most in its history — while expanding its network to a total of 1,010 km of conduit across the region. Its goal of reaching more than 1,600 km is within sight, driven by the support of the Universal Broadband Fund, a $3.225 billion investment by the Government of Canada designed to help provide high-speed internet access to 98 per cent of Canadians by 2026 and achieve a national target of 100 per cent access by 2030.
YorkNet’s approach to laying conduit is equally significant. It involves extra inner microducts within each conduit during initial construction. Those are left empty until demand rises. The `design reduces future installation and maintenance costs, minimizes disruption to other public infrastructure, and protects fibre lines — which can transmit data up to 1,000 times faster than traditional copper cables or satellite internet services — from environmental damage.
“Having that connectivity is super important,” Wheatle explains. “High volumes of data and huge bandwidth will really drive the economy of the future.”
While already empowering regional services, YorkNet’s fibre also serves a wider community. It’s used by ORION, the Toronto-based not-for-profit organization that connects post-secondary and research institutions in Ontario so they can collaborate, as well as the national CANARIE network, which links Canadian universities, colleges, research hospitals, science facilities, and other academic and research centres with each other and to global research and education networks.
“For organisations that are looking to take their connectivity to the next level, partnering with us is an option,” Wheatle says.
He adds that, as the affiliations with ORION and CANARIE demonstrate, YorkNet is often one piece of a larger puzzle. “YorkNet wouldn’t be necessarily your only provider or solution to whatever challenge you’re trying to accomplish, but because our network covers the entire region it allows for route diversity. You could leverage YorkNet to traverse the region to get north, east, west, or south.”
High volumes of data and huge bandwidth will really drive the economy of the future.—Jonathan Wheatle, Director of Economic Strategy, York Region
In practice, that backbone is what lets the region’s full stack function as a single system: chips designed in Markham, models trained at SciNet, and products shipped by companies across the GTA all rely on being able to move vast amounts of data quickly and reliably across the same public network.
Doing big things through shared compute
On top of that connectivity, SciNet adds shared compute at a scale that most organisations can’t replicate.
SciNet is the supercomputer centre at the University of Toronto. Funded by the university as well as the federal government and Government of Ontario, the centre is host to Trillium, a 241,056-core, 252-GPU cluster that is officially put into service in August 2025. For AI and other computationally intensive fields, it represents a step change in available capacity.
“Today, every field of research has a computational component,” explains Daniel Gruner, SciNet’s Chief Technical Officer. “It doesn’t matter what you’re doing. You could be doing social sciences, medical sciences, engineering, you name it.” But with that, he adds, there’s an important human component. “Something I always tell people is that the most important thing we do is not through providing the physical resources. A computer is a computer. The most important thing that we do is help train people how to use the resources, how to do their workflows, and how to do their science when it’s computational science.”
That said, Gruner allows that Trillium is not “just a computer.” It’s substantially larger than SciNet’s earlier workhorse cluster.
“If nothing else, it’s much bigger!” he says. “And better. We have about three times the compute capability that our previous system had.”
What that means is that people can do “really big things,” as Gruner puts it. It’s also important to note that as a public-interest compute centre, SciNet is strategically different from a commercial cloud for AI. It prioritizes digital sovereignty, equitable access, and the development of AI for the public good over purely commercial interests.
He offers an example.
“Just before we opened Trillium for general use, there were two groups that were very interested in pushing the limits of what the system can do for them and their science. One group of climate scientists at the University of Toronto wanted to run a full ocean simulation at high resolution. For context, they were running computations on the full 240,000-plus cores of the system. Single runs using the whole computer is no small feat. You need code that can work at that scale. You also need to make it work efficiently. And you want to produce data out of those runs. You’re talking petabytes from simulations like that. So that’s the kind of capability that we’re providing.”
Meanwhile, SciNet is already anticipating its next bold endeavour. In November 2025, the University of Toronto announced that it will receive $42.5 million in federal funding for a new compute cluster designed for AI workloads, which SciNet will host and operate. The cluster is expected to be delivered and installed in the first half of 2026.
Building now for what comes next
When the new cluster is officially online, it will add significantly to SciNet’s capabilities and help strengthen the region’s already substantial AI infrastructure.
So, too, will continuing investments in silicon expertise, as AMD’s example illustrates. At the same time, ventureLAB is channelling public funds to empower entrepreneurs and companies exploring new hardware and semiconductor applications, while YorkNet is building a dark-fibre network that enables data to move efficiently and reliably when needed now and in the future. Crucially, none of these pieces operate in isolation: the same engineers, founders, and researchers move between them, carrying knowledge and capability up and down the stack.
Together, those pillars of silicon, connectivity, and shared compute form a stack that gives the Toronto Region a durable structural advantage. It’s an infrastructure base that supports today’s AI workloads and leaves the region well positioned for whatever comes after the current wave.
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