Kernel-level optimization for any hardware
Kernelize gives hardware companies a fast, repeatable path to production-ready inference for every new model.
AI Inference Stack
inference layer
inference engine (vLLM)
execution framework (pytorch)
Kernel layer
hardware vendor layer
vendor software
ai accelerator hardware
Built with the teams shaping AI inference
Triton compiler collaboration
Triton compiler collaboration
Open kernel platform support
guided kernel development
A common workflow for every chip
Kernelize gives kernel developers a faster path from new models to production-ready inference. The platform identifies model gaps, guides kernel refinement, and connects improvements back into PyTorch, Triton, and vLLM. The result? Faster bring-up with less device-specific rework.
inference analysis
Identify kernels
blocking model support
Analyze models against the existing kernel layer of the AI inference stack. Decompose each model into operators, kernels, and execution paths, then identifiy the missing or underperforming kernels that prevent production-ready model support.
kernel optimization
Generate and refine chip-specific kernels
Use Triton to generate, test, and refine kernel strategies before committing to a vendor-specific implementation. Proven kernels move into each hardware vendor's native flow for further refinement and production deployment.
AI inference stack integration
Deploy optimized kernels through standard interfaces
Connect kernel improvements back into the AI inference stack through the standard interfaces in the PyTorch ecosystem. Optimized kernels can be validated, packaged, and deployed without a fragmented software path for every chip.
heterogeneous inference
Optimize inference across models, workloads, and chips
Kernelize starts by helping each chip support new models through the kernel layer. Over time, the same analysis and optimization loop enables heterogeneous inference: comparing chips, tuning kernels for real workloads, and matching each deployment to the hardware that delivers the best performance, cost, and power profile.
Compare between different chips
Identical execution semantics across runs
Consistent kernel-level metrics
Comparable reports for latency, throughput, and memory
Optimize for deployment
Tune for production workloads
Balance latency, cost, power and capacity
Reuse optimizations across models and chips
Match workloads to hardware
Identify best chip for each model and workload
Route workloads based on context or requirements
Adapt as workloads and hardware evolve
Support heterogeneous fleets
Add new chips without splitting workflows
Keep older hardware useful longer
Reduce dependence on one vendor stack
Getting Started
Start supporting the latest models
Start with a focused jumpstart project to prove what Kernelize can do for your chip. In about a month, we analyze a target model, identify the kernel gaps blocking support, and define the path to production-ready inference.
After the jumpstart, hardware vendors can license the Kernelize Platform at a fixed price per chip SKU, with forward-deployed engineering support to adapt the platform to each architecture, compiler, runtime, and kernel layer.