Sim Theory offers two SDKs—Thunder for high‑throughput cloud and enterprise workloads and Lightning for low‑latency consumer applications—that automatically parallelize code and exploit SIMD to fully utilize existing CPU cores. By optimizing thread scheduling and memory access, the libraries can cut compute time by up to 90% on the same hardware, helping cloud providers, enterprise IT teams, and performance‑critical app developers reduce costs and accelerate processing.
Funding
$2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many organizations run compute workloads on CPUs without fully utilizing the available cores, leading to excess cloud costs and slower processing times. Inefficient parallel data processing hampers scalability for both enterprise cloud services and consumer-facing applications.
Solution
Sim Theory provides two SDKs—Thunder for cloud and enterprise compute and Lightning for application and consumer experiences—that tap into the full potential of existing CPU cores. By optimizing thread scheduling, memory access patterns, and SIMD utilization, the SDKs accelerate parallel data processing without requiring additional hardware. The libraries integrate with standard development environments and can be deployed on existing virtual machines or on-premise servers, delivering up to 90% reduction in compute time on the same instance. Sim Theory offers a free pilot program and assessment to demonstrate performance gains before adoption.
Target Audience
Primary customers are cloud service providers, enterprise IT teams running large‑scale compute jobs, and developers of performance‑critical consumer applications.
Features
- Thunder SDK optimized for high‑throughput cloud and enterprise workloads
- Lightning SDK designed for low‑latency consumer and application use cases
- Automatic core‑level parallelization and SIMD exploitation to maximize CPU efficiency
- Compatibility with common programming languages and build systems
- Drop‑in integration with existing codebases, requiring minimal code changes
- Performance monitoring tools that report core utilization and speedup metrics