Geometric
Geometric helps machine learning teams maximize GPU utilization by automatically discovering and verifying CUDA kernel optimizations. The platform analyzes model workloads, identifies performance bottlenecks, and ships verified speedups directly as GitHub pull requests, enabling seamless integration into existing development workflows. Built for organizations deploying ML at GPU-fleet scale, it reduces the manual effort required for kernel-level performance tuning.
- Artificial Intelligence
- Data & Analytics
- Developer Tools
- Software Only
Funding
Founders
Product
Problem
Machine learning teams often underutilize their GPU infrastructure because identifying and implementing optimized CUDA kernels is a highly manual, time-intensive process that requires deep expertise. This results in significant wasted compute spend and slower model training and inference cycles, especially for teams operating at fleet scale.
Solution
Geometric provides an automated platform that discovers and verifies CUDA kernel optimizations for ML workloads. The system analyzes model code and runtime behavior to identify performance bottlenecks, then automatically generates optimized kernel implementations. These verified speedups are delivered directly as GitHub pull requests, allowing teams to integrate them into their codebase with minimal friction. By automating the entire optimization workflow, Geometric enables engineering teams to achieve substantial performance gains without dedicating specialized engineers to kernel development.
Target Audience
Primary customers are machine learning engineering teams and platform teams at organizations deploying ML models at GPU-fleet scale, particularly those in AI infrastructure, large-scale model training, and high-throughput inference environments.
Features
- Automated CUDA kernel discovery that scans model codebases to identify suboptimal operations and replacement opportunities
- Verification pipeline that benchmarks proposed kernels against existing implementations to ensure correctness and measure actual speedup
- Direct GitHub pull request integration that packages verified optimizations as ready-to-merge code changes
- Fleet-scale analysis capabilities designed for organizations managing large GPU clusters and multiple ML models
- Performance monitoring that tracks speedup impact across training and inference workloads over time