The proving ground
where workloads
meet hardware.
Build your roadmap on actual data,
not guesswork.
AI evolves faster than
the hardware it runs on.
While models are released every week,
silicon cycles can take years.
We measure
the gap.
Matching models to hardware and hardware to models — from today's chips to next-gen silicon, with the partners who build both. Here's what we're finding.
How many devs can you
fit on a GPU?
Tokens are getting expensive. We ran the numbers on owning the hardware instead: TCO, concurrent users, and real task resolution for teams that aren't hyperscalers.
Roofline
to reality.
The "back of envelope" math says one thing, vLLM's counters say another. Where theory and hardware actually meet.
How to fit more
devs on a GPU.
Model, harness, serving stack: we varied each and measured what moved. Which levers pay off, counted in tasks resolved, not tokens generated, before you touch anything exotic.
Your roadmap,
built on data.
Whatever you build — the models, the infrastructure, the application on top, or the hardware itself — benchmark numbers and real behavior diverge in different places. We bring them together so you can make informed decisions.
AI model
builders
Your checkpoints, running on silicon before you commit to it.
Infrastructure
providers
How your stack holds up under workloads that don't exist yet.
Hardware
builders
Independent numbers on your chip or system, the kind buyers or investors actually trust.
Application
builders
What your app really costs per request, before you commit to a stack.
Real conditions.
Clear signal.
Clarity
starts here.
Tell us what you're running, bring a workload, a chip, a system, or a question. Or just follow the insights and get results delivered to your inbox.