Win benchmarks on new models
Kernelize gives hardware companies a faster path from initial bring-up to meeting target performance.
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 model workflow
A common platform for every chip
Kernelize breaks model bring-up and optimization into small, incremental tasks. The platform identifies gaps, creates focused reproducers, and reconnects fixes into PyTorch, Triton, and vLLM.
inference analysis
Decompose full-model issues
Analyze model failures and bottlenecks, then create focused reproducers that isolate the smallest model, layer, operator, or kernel causing the issue.
model optimization
Fix issues in isolation
Use structured agents, PyTorch, Triton, and compiler tools to generate, test, and refine fixes before committing to a lower-level implementation.
inference stack integration
Reconnect fixes to the full model
Validate fixes in context and reconnect them into PyTorch, Triton, vLLM, and the hardware vendor stack so improvements carry back to production.
heterogeneous inference
Scale the platform across models, workloads, and chips
Kernelize starts with an agentic platform for model bring-up and optimization. Over time, the same platform grows for each chip to compare hardware, tune deployments, and route workloads based on performance, cost, and power.
Compare between chips
Identical execution semantics across runs
Consistent correctness and performance 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 based on context or requirements
Adapt as models 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
Begin with a target model and deployment goal. Kernelize creates focused reproducers, validates fixes, and shows the path from initial bring-up to target performance.
After the evaluation, hardware companies can expand to the Kernelize Platform with agentic workflows and support for each architecture, compiler, runtime, and inference stack.