I’ve had the same Razer mouse since ninth grade. I’m now a sophomore in college.
It’s one of those things that has just always been there. I’ve used it for everything from playing COD and Valorant to clicking through college applications, without ever thinking twice about it.
Recently, though, I came across something from Razer as an Akash Network ambassador that made that feel a bit like a full-circle moment, even if the connection itself is mostly indirect.
Razer recently announced an AI collaboration with Akash around their AIKit system. AIKit is an open-source toolkit that lets developers build and deploy AI features like image, video, and audio generation across different hardware setups. It’s designed to work both locally and at scale, depending on the type of workload.
One example they shared was “AVA Mini,” a consumer AI experience where users upload photos and receive AI-generated outputs back in seconds. What made this more interesting than a typical AI demo is that it ran as a live campaign, processing over 11,000 image generations in a five-day period, with ~3.24 second average turnaround per image. That scale matters, because it immediately turns what looks like a simple AI feature into an infrastructure problem.
These workloads depend on real-time GPU inference. And once you start handling thousands of concurrent requests, the underlying constraints begin to reveal themselves. According to the announcement, conventional cloud APIs for this kind of workload typically cost between $0.03 and $0.15 per image. At scale, that becomes one of the dominant constraints on whether something like this is even economically viable.
To make the campaign work, Razer partnered with Akash Network and deployed parts of the inference pipeline through AkashML, Akash’s managed GPU inference service. This allowed workloads to run across a distributed network of consumer GPUs, including high-end hardware like RTX 4090 and 5090-class systems.
The result was a reported reduction in inference cost to around $0.01 per image, which is roughly 15x lower compared to standard cloud APIs, while still maintaining real-time performance under burst demand.
For a long time, compute has felt like a binary choice: either you run things locally or you rely on centralized cloud infrastructure. On one hand, local gives you control, but limits scale. Cloud gives you scale, but comes with fixed cost (usually high) and abstraction.
This setup doesn’t fit into either category.
AIKit is explicitly designed to run locally on developer hardware, meaning experimentation can happen without immediately relying on cloud infrastructure. But when the workload scales into production-level demand, Akash extends that same system outward into distributed compute resources. So instead of choosing between local or cloud, compute starts to move between both depending on the workload itself.
That shift becomes more interesting when you connect it to how people actually build. Even in small settings, like in a recent class hackathon I did where we were given Google Cloud credits for deployment, compute constraints quietly shape behavior. You think differently about experimentation and scope when there’s a cost attached to it; you optimize early and don’t “go for it.” You hesitate before trying things that would even risk wasting resources.
At scale, those same constraints remain, becoming embedded in infrastructure decisions that determine what gets built and how quickly.
Now, I don’t think the shift here is about replacing cloud infrastructure, as that wouldn’t make sense for every case. It’s more that the way compute is being accessed and provisioned is becoming less centralized.
Instead of committing upfront to a single provider or a fixed infrastructure, developers can start locally, scaling into distributed systems when needed, and moving between environments based on cost and demand in real time.
It shifts compute from something you “allocate” in advance to something you “route” dynamically based on need.
Learning more and more about Akash through being an ambassador has shown me that nothing fundamentally new has appeared, but that a very familiar set of tools — AIKit, cloud APIs, GPU inference — can now be composed in a way where compute isn’t tied to a single centralized layer anymore.
And in a strange way, it made me think back to something as simple as a mouse I’ve used for years.
Not because it has anything to do with compute itself.
But because it represents how long I’ve been interacting with technology without thinking about what sits underneath it.
https://www.razer.com/newsroom/ai/razer-aikit-new-features
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