How Should Canadian Businesses Plan AI GPU Spending Before Euclyd Arrives in 2028?

A small Dutch startup called Euclyd has secured Samsung as a backer in its attempt to challenge Nvidia's grip on AI GPUs — but its inference chip isn't expected until 2028. For Canadian businesses currently budgeting in CAD for AI infrastructure, that gap between now and 2028 is the real planning question, not the chip itself.

What is the Concept

Euclyd is one of several emerging challengers targeting Nvidia's dominance in AI inference chips — the hardware that runs already-trained models in production. Samsung's backing gives the venture real manufacturing credibility, unlike many speculative chip startups. But a 2028 launch means this is a long-horizon supply story, not something that should change what a Canadian business does with its AI budget in 2026.

For companies in Toronto, Vancouver, and Montreal weighing multi-year AI infrastructure commitments, the practical question isn't "should we wait for Euclyd" — it's "how do we avoid locking into today's Nvidia-driven pricing while a more competitive chip market is visibly forming behind it.

Why It Matters Now (2025–2026 Context)

Nvidia's pricing power has pushed up AI compute costs for Canadian enterprises throughout 2025, and that pressure has been especially acute for mid-sized firms without the purchasing scale of the big banks or major telcos. News of credible challengers like Euclyd, backed by a manufacturing heavyweight in Samsung, gives Canadian finance teams a genuine reason to negotiate shorter contract terms in 2026 rather than lock in long-term hardware or cloud GPU rates.

This matters most for Canadian SMEs and growth-stage companies that are the most exposed to current Nvidia pricing and stand to benefit most once real competition reaches the market.

How AI Is Changing This

Inference — running a trained model in production, as opposed to training it — is where most Canadian businesses actually spend their AI compute budget. That's precisely the segment Euclyd and similar challengers are targeting, since inference workloads are more predictable and better suited to specialized, purpose-built silicon. As more inference-focused chips reach the market from 2027 onward, expect a genuine price war in a segment where Nvidia has so far faced limited competition.

Here's the contrarian point worth making directly: most Canadian companies don't need to own any GPU hardware at all. Renting AI compute through cloud providers and staying contractually flexible matters far more right now than trying to pick a hardware winner three years before it ships.

Real-World Examples

Consider a Canadian insurance company running fraud-detection inference at scale. Signing a rigid three-year Nvidia-based contract in 2026 secures capacity but forfeits any pricing benefit if Euclyd or another challenger disrupts the market by 2028. A Canadian retailer using AI for demand forecasting, by contrast, sized its compute needs conservatively and uses flexible, cloud-based GPU access — it can switch providers or hardware generations as the market shifts, with minimal lock-in risk. Samsung's manufacturing scale is exactly why its backing of Euclyd is credible rather than speculative; it's the kind of partner capable of actually getting a chip into production at volume.

The founder mistake here is assuming today's GPU market structure is permanent and building long-term infrastructure bets around it.

Practical Insights / Actions

Canadian businesses should run what we call a Compute Flexibility Check before any major AI infrastructure commitment: confirm contract length against realistic hardware refresh cycles, confirm whether workloads are inference-heavy (where new competition matters most) or training-heavy (where Nvidia's lead persists longer), and confirm there's an exit or renegotiation clause tied to shifts in market pricing.

The hidden opportunity is negotiating leverage: Canadian procurement teams can now credibly cite emerging, Samsung-backed competition when negotiating current Nvidia or cloud GPU pricing, even years before any rival chip actually ships.

Future Outlook

Expect 2026 to be a year of positioning rather than disruption — Nvidia keeps its lead, but credible challengers like Euclyd give Canadian buyers real negotiating leverage for the first time in years. By 2028, when Euclyd's chip is expected to ship, Canadian businesses that kept their AI infrastructure contracts flexible will be best positioned to capture the pricing benefit of genuine competition.

Conclusion

The takeaway for Canadian businesses isn't to wait for Euclyd — it's to avoid rigid, long-term AI infrastructure commitments while the chip market is visibly becoming more competitive. RP SoftTech helps Canadian SMEs design AI infrastructure and cloud strategies that stay flexible as the hardware market evolves, so today's spending decisions don't become tomorrow's overpriced liability.