GPU kernel autotuning and code-generation software for supported Triton tile configurations.
See what it solvesTHE PRODUCT, IN PRACTICE
When a GPU kernel is a bottleneck, choosing a configuration and turning it into usable code should be one connected task. SpinKernel explores the supported Triton configuration space and carries the selected choice into code generation. It gives kernel engineers a practical output to benchmark and validate, rather than another recommendation that must be implemented from scratch.
Comparing configurations one at a time can turn a bounded kernel task into repeated experimentation.
SpinKernel organizes configuration selection for the supported kernel scope, giving the engineer a selected candidate to test rather than an unstructured list of possibilities.
A promising setting still has to become code that can enter the development workflow.
The selected configuration feeds the included code generator, connecting the choice to a concrete implementation candidate.
A setting that looks good in isolation may not help the full pipeline or meet its correctness requirements.
SpinKernel supplies the candidate for comparison in your real workflow. Acceptance includes numerical correctness and complete-workload performance, not a configuration count presented as a speedup.
Autotuning over the product’s supported Triton tile configurations.
A concrete choice for the kernel development and evaluation workflow.
Code produced from the selected configuration through the included generator.
A candidate implementation ready for your existing correctness and performance checks.
SpinKernel is fully available with CLI, MCP and headless access. Use the interface that fits your terminal, your agents or your automated workflow.
Run the product from a terminal or script, alongside the tools your team already uses.
Get CLI accessConnect MCP-compatible clients and agents to the product’s exposed capabilities.
Get MCP accessOperate without a graphical interface in unattended, scheduled or pipeline-driven work.
Set up headless accessProduct-specific inputs, hardware requirements and result guarantees still apply. An access mode does not change your license or deployment scope.
Explore all product interfacesAn engineering team has profiled its pipeline and isolated a compatible kernel that merits tuning.
Illustrative customer use—not a new benchmark or completed customer deployment.Define the supported kernel, hardware and test inputs.
Use SpinKernel to select a configuration and generate a candidate.
Check the output and benchmark the candidate inside the full pipeline.
Explore the product workflow, from your input to a result you can use.
Begin with a field of candidate tile configurations.
Interactive explanation. This illustration does not execute customer workloads or display measured performance.
The included configuration space is product scope, not a performance guarantee. Generated code still needs the customer’s correctness and deployment tests. SpinKernel does not claim to automatically rewrite every arbitrary application.
$90,000/year is the retained standalone price proposal for its named scope. The platform plans buy qualified workflows and capacity, not a second charge every time a compatible method is selected. Existing bundle inclusions do not expand automatically.
Fully available with CLI, MCP and headless access. Runtime requirements, supported operations and license scope remain product-specific.
One named production deployment. Shared engine rights for this application included.
Scope & packagesBring a representative workload and the result your team needs. We’ll define the product, deployment and engineering scope around that task.
Activate this capability inside a qualified ai serving workflow. Compatible methods and required shared checking are included once within that workflow; the product remains independently purchasable.
Capacity, release, environment, result contract and operating responsibilities are specified before activation. Choosing an additional method is not permission to add an unrelated workload or unqualified deployment.
Explore AI Serving Choose workflow capacityCommission the model, integration and acceptance work needed for your task. Existing products stay independently available.
Use the software directly or ask us to scope the implementation.
One capability. A connected workflow.
A team to put it into practice.