HyperBench AI
HyperBench AI provides continuous hardware-in-the-loop testing that lets engineering teams validate software on real hardware, automatically investigating bugs, reproducing failures, and testing candidate fixes. The platform traces issues across code and hardware, delegates fixes across repositories, and generates draft pull requests for review. Reported results include a 44.8× speedup on a shared-weight BF16 matmul in PyTorch.
- Developer Tools
- Artificial Intelligence
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Engineering teams struggle to validate software reliability and performance on real hardware, relying on manual or infrequent testing that misses regressions and bottlenecks. When hardware and software interact, issues often surface only in production, leading to costly downtime and slow iteration cycles.
Solution
HyperBench AI provides continuous hardware-in-the-loop monitoring that keeps software and hardware in a constant testing loop. The platform automatically investigates bugs when they are detected, reproduces failures, and tests candidate fixes directly on real hardware. By leveraging AI to drive the test-fix-retest cycle, it eliminates the manual slowdown of traditional lab validation. The platform traces issues across code and hardware, captures failures on the customer's own bench, delegates fixes across repositories, and retests on hardware while generating draft pull requests for review. Reported results include a 56.3-second LLVM loop-chain compile reduced to 23 milliseconds on Graviton3, and a shared-weight BF16 matmul made 44.8× faster in a PyTorch benchmark.
Target Audience
Primary customers are engineering and platform teams building performance-critical software, including ML infrastructure, compiler toolchains, and cloud-native applications that must be validated on specific hardware architectures.
Features
- AI-driven bug investigation and failure reproduction on real hardware targets
- Continuous hardware-in-the-loop testing that combines hardware and software validation in one workflow
- Automated fix verification that tests patches directly on the target platform
- Cross-repository fix delegation with draft PR generation for human review
- Bring-your-own-bench support, allowing customers to use their existing hardware infrastructure
- Evidence at every handoff, with code, test results, and recordings presented together for review
- Performance benchmarking and bottleneck detection, with published results including 44.8× speedup on shared-weight BF16 matmul in PyTorch
- Compile-chain optimization support, demonstrated by a 56.3s → 23ms reduction in an LLVM loop-chain compile test on Graviton3