Turn faster kernels
into a smaller GPU bill.
A faster model is reclaimed capacity, and reclaimed capacity is money you stop spending. Cost intelligence connects every speedup Kernalin proves to the exact GPU spend it lets you cut — the number that ends up on your P&L, not just your dashboard.
Faster kernels. Reclaimed capacity. Lower bills.
Every figure on this page is illustrative — an example of the model, not a customer claim.
Illustrative UI. Figures are examples, not customer claims.
From silicon busy to dollars saved.
GPU utilization
How much of the silicon your workload actually keeps busy — GPU and memory utilization from the driver, plus SM occupancy and tensor-core duty cycle where hardware counters are allowed.
Workload efficiency
Useful FLOPs per watt-second, once stalls, uncoalesced traffic, and launch overhead are subtracted out.
Inference cost
Efficiency mapped onto instance price — the real dollars behind a request path, not a rack-level average.
Cost per token
Normalized to the unit you actually bill and reason about, so a kernel win becomes a line item you recognize.
Potential savings
Reclaimed capacity expressed as spend you no longer have to provision — the payoff of a verified speedup.
The waste that never hits a dashboard.
Fragmented VRAM
Allocator churn leaves memory that is technically free but unusably scattered. Batch sizes shrink, a second GPU gets provisioned, and the invoice grows for capacity you already paid for.
Uncoalesced access
Threads in a warp touch memory in a pattern the hardware can’t merge. Effective bandwidth collapses to a fraction of spec while the meter reads “busy.”
Idle GPUs
Devices held by a job that spends most of its time waiting on host sync, small launches, or the network. High reservation, low real work.
Launch overhead
A pipeline of tiny kernels where per-launch cost rivals the compute. The dashboard shows utilization; the profiler shows the gaps between the bars.
Detection, heatmaps, and savings you can act on.
Underutilized-GPU detection
Rank devices by real work delivered against reserved capacity, and surface the ones quietly burning budget while a dashboard shows them “allocated.”
Utilization heatmaps
A time-by-device view of occupancy and bandwidth, so a recurring 2 a.m. trough or a chronically half-fed replica becomes obvious at a glance.
Savings insights
Every efficiency finding is tied to a projected dollar figure and the profiling evidence behind it — so a rewrite is justified before anyone spends time on it.
Prove the speedup. Then prove the savings.
Cost intelligence is in preview. Join early access to shape how verified performance maps to your GPU bill.