Inference platform working across chips

Compare inference performance between different chips using the same software stack

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Docker

vLLM

Triton kernels

Pytorch

Triton compiler

Triton backend plugin

Device specific code

Device
Kernels

Device
compiler

VLLM Backend
Plugin

Device
Program

Run a consistent inference platform across chips by keeping the core software the same and swapping only chip-specific plugins.

Evaluate inference performance by running full models and production workloads instead of isolated benchmarks.

Integrate with your existing ML stack using official Triton backend plugins tested by Kernelize and certified to work in PyTorch and vLLM.

Inference Evaluation

Consistent results across hardware

Kernelize enables apples-to-apples inference evaluation by preserving execution semantics, workflows, and reporting across hardware platforms.

Identical execution semantics across runs

Same reports for latency, throughput, and memory behavior

Reuse existing models and workflows

Avoid vendor-specific runtimes and rewrites

Shorten evaluation and decision cycles

Uses official PyTorch and vLLM plugins

Standard model formats

No custom kernel languages required

Built on open-source Triton plugins

Consistent behavior

Clean separation between platform and hardware

Get Started

Tell us about your inference stack and hardware needs. We’ll help you evaluate how Kernelize can support your models across more hardware, faster.

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