Standardkernel
Standardkernel offers an automated platform that generates low‑level CUDA + PTX kernels optimized for modern GPU and TPU features such as tensor cores and specialized memory units. By using research‑grade models and the KernelBench suite, it produces compute‑bound kernels that match or exceed vendor library performance and delivers ready‑to‑run binaries with detailed performance reports for AI infrastructure teams and framework developers.
- Artificial Intelligence
- Developer Tools
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Developing high-performance AI kernels for GPUs and TPUs requires deep expertise in low‑level hardware programming, extensive profiling, and manual tuning. Skilled kernel engineers are scarce and costly, leading to long development cycles and suboptimal utilization of modern accelerator capabilities.
Solution
Standardkernel provides an automated kernel generation platform that produces low‑level CUDA + PTX code optimized for the latest accelerator features such as tensor cores and specialized memory units. By leveraging research‑grade models and a curated benchmark suite, the system generates compute‑bound kernels that approach or exceed vendor library performance without waiting for compiler or ecosystem updates. Users receive ready‑to‑run kernel binaries along with performance reports, enabling immediate integration into AI workloads. The platform continuously adapts to new hardware releases, ensuring that models can exploit emerging silicon capabilities as soon as they become available.
Target Audience
Primary customers are AI infrastructure teams, deep‑learning framework developers, and large‑scale model training organizations that need custom, high‑performance kernels to maximize accelerator utilization.
Features
- Automatic generation of CUDA + PTX kernels targeting compute‑bound workloads on modern GPUs (e.g., H100 BF16 matmul, attention)
- Fine‑grained hardware control that leverages tensor cores, tensor memory, and inline PTX instructions for maximal efficiency
- Integrated benchmark suite (KernelBench) to validate and compare generated kernels against vendor libraries
- Fusion engine that discovers multi‑kernel optimization strategies, such as combined matmul‑SiLU pipelines for transformer feed‑forward networks
- Export of compiled kernel binaries and detailed performance metrics for seamless integration into existing AI stacks