Our Position on GPU Compute
We regularly receive requests for GPU-enabled instances, and we appreciate the interest. We've made a deliberate decision not to offer GPU workloads at this time, and we want to be transparent about why.
The GPU market is in rapid flux
The AI/ML hardware landscape is evolving faster than any other segment in computing history. High-end GPUs that cost $100,000 two years ago now sell for $20,000 on the secondary market—and are significantly outperformed by newer generations. This creates an unprecedented depreciation curve that doesn't align with sustainable infrastructure economics.
Unlike traditional server hardware, which delivers reliable value over a 5-7 year lifecycle, GPU hardware for AI workloads can become uncompetitive within 18-24 months.
Pricing doesn't reflect true costs
Major cloud providers are currently pricing GPU inference below actual operating costs as they compete for market share. This is a strategic choice funded by their scale and access to capital markets. Matching those prices would require us to either operate at a loss or deliver an inferior experience—neither option serves our customers well.
The technology landscape is shifting
NVIDIA currently dominates GPU compute, but the inference market specifically is seeing significant disruption:
- Purpose-built ASICs like Google's TPUs are proving highly competitive
- New architectures from multiple vendors are challenging traditional GPU approaches
- Major cloud providers are developing custom silicon for their own workloads
We believe the hardware that wins the inference market in three years may look quite different from what's available today.
We're ready when the market is
BinaryLane runs on a fully proprietary cloud platform built in-house. Unlike providers reliant on third-party software, we control our entire stack—from billing systems to compute orchestration. This means adding GPU support for inference workloads is straightforward from a technical perspective. The infrastructure, APIs, and billing integration can be implemented relatively quickly when the time is right.
Our decision to wait isn't about capability—it's about delivering value. When we add GPU compute, it will integrate seamlessly with the same panel, CLI, and API you already use, with the same transparency and reliability you expect from us.
Our commitment to you
BinaryLane has always focused on doing a few things exceptionally well: reliable compute, fast storage, and excellent Australian connectivity. We're a privately funded Australian company, which means we invest where we can deliver genuine, sustainable value—not where hype cycles suggest we should.
We'll continue to monitor the GPU market closely. When the technology stabilises and we can offer GPU compute that meets our standards for reliability, performance, and value, we'll be ready.
In the meantime, if you need GPU workloads, we'd rather point you to a provider who can serve you well today than offer something that doesn't meet our standards.
Questions?
If you'd like to discuss your specific requirements, our team is always happy to chat. Sometimes what looks like a GPU problem has a compute-optimised solution—and sometimes it genuinely needs a GPU. We'll give you an honest answer either way.