Model-specific kernels
Profile each model and hardware pair, then tune the operations, memory movement, and execution path that dominate its workload.
↘Model-specific inference systems
Every modelhas a hiddenruntime.
General runtimes flatten every model into the same machine. We go the other direction—inside the graph, beneath the framework, toward the shortest possible path from request to token.
Enter the runtime↗MODEL / HARDWARE / KERNEL
TUNED AS ONE SYSTEM
TUNED AS ONE SYSTEM
01 / THE THESIS
The model is not
the machine.
THE UNSEEN LAYER
Between weights and tokens sits an execution path most providers leave generic. That path is where latency accumulates, memory moves twice, and economics disappear.
Kernelwright rewrites it—model by model, bottleneck by bottleneck.
02 / THE METHODNothing generic
Nothing generic
survives the profile.
Whole-system optimization
Optimize kernels, quantization, KV cache, graph capture, batching, queueing, and admission control as one serving system.
↘Compatible by default
Preserve OpenAI-compatible chat completions, streaming, tool calling, structured outputs, and usage accounting.
↘03 / THE ARENA
Winning is
a measured state.
Kernelwright’s kernel work is winning in Cacheon and other GPU performance competitions. The discipline is the same one we bring to serving systems: isolate the real bottleneck, beat the incumbent on fixed hardware, preserve fidelity, repeat.
How Cacheon evaluates kernelsCROWNED
04 / THE CONTRACT
Fast is only useful
when it routes.
Kernelwright is built for inference marketplaces and product teams that need a narrow model catalog, predictable interfaces, transparent usage accounting, and capacity that behaves under load.
Operating principles00
Model qualification
SELECTLicense / workload / hardware fit
01
Runtime tuning
TUNEKernels / cache / batching
02
Compatibility
PROVEStreaming / tools / structured output
03
Production envelope
PUBLISHSustained throughput / admission control
05 / INTEGRATE