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Kernelize

KERNELIZE PLATFORM

A Triton-based platform for portable AI inference

Kernelize provides a Triton-based platform for running inference across diverse hardware. Triton defines the kernel language, Triton Extensions handle chip-specific optimization, and the Kernelize platform manages how this works across devices.

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Docker

vLLM

Triton kernels

Pytorch

Triton compiler

Triton extension

Device specific code

Device
Kernels

Device
compiler

VLLM
Plugin

Device
Program

the kernel language

Triton

By separating how kernels are written from how they are executed, Triton provides a stable interface that higher-level software can depend on as hardware evolves.

Kernel abstraction

Triton defines how performance-critical kernels are expressed using tile-based semantics, independent of hardware implementation details.

Compiler-based optimization

Triton kernels are compiled into device-specific execution models rather than interpreted or dispatched at runtime.

Hardware-agnostic source

Triton kernels remain hardware-agnostic at the source level, with execution details defined downstream.

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chip-specific optimization

Triton Extensions

Each extension maps the Triton language onto a device’s execution model, controlling lowering, scheduling, and hardware-specific behavior.

By isolating this work in extensions, higher-level software remains unchanged as new hardware is introduced.

Chip-specific layer

Each Triton Extension targets a specific chip, mapping Triton kernels onto that device’s execution model.

Optimization strategies

Extensions select and configure hardware-specific optimization, scheduling, and execution behavior without changing kernel source code.

Isolate hardware complexity

Hardware-specific details are isolated in extensions so models and higher-level software remain unchanged.

View on github

consistent compute across chips

Kernelize

The Kernelize platform ensures that kernels, extensions, and higher-level software evolve together without fragmenting the software stack.

Chip lifecycle management

Standardized tooling to build, validate, and maintain Triton Extensions as hardware and compilers evolve.

Heterogeneous hardware support

Run Triton-based inference across mixed hardware environments using a single, consistent software approach.

Portable kernel support

Structure and conventions that help kernels remain portable across devices as hardware capabilities evolve.

Upstream version alignment

Releases aligned with official Triton and PyTorch versions to ensure long-term compatibility and stability.

unlocking heterogenious clusters

Heterogenous inference optimization

The Kernelize platform enables consistent analysis across devices, helping teams optimize tradeoffs as inference workloads move to different hardware.

Compare between different chips

Identical execution semantics across runs

Use the same Triton kernel for consistency

Same reports for latency, throughput, and memory behavior

Deployment tradeoffs

Understand performance and efficiency tradeoffs

Evaluate cost and capacity implications

Shorten evaluation and decision cycles

Incremental adoption

Validate behavior before shifting workloads

Avoid fragmenting the software stack

Support long-term heterogenous clusters

No vendor lock-in

Built on open-source Triton plugins

Consistent behavior

Clean separation between platform and hardware

Getting Started

Triton Extension bring-up

Some hardware platforms already have Triton support.
In those cases, teams can adopt the Kernelize platform directly to manage extensions, maintain portability, and support heterogeneous deployments.

For hardware without existing Triton support, Kernelize provides a short-term on-ramp to establish an open-source Triton Extension as the foundation for using the platform.

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Kernelize