Cerebras Systems designs and manufactures wafer-scale AI chips that consolidate the compute capacity of dozens of GPUs into a single device. Founded in 2015, the company's core architecture is 56 times larger than standard GPUs, addressing the operational complexity of distributed training and inference by offering programmability equivalent to a single-device system while delivering multi-GPU performance. This approach collapses the network bottlenecks and synchronization overhead inherent in GPU clusters, enabling users to run large-scale ML workloads without orchestrating hundreds of accelerators.
The company's technical stack spans the full systems hierarchy: custom silicon (wafer-scale chip architecture), compiler infrastructure (MLIR, LLVM IR, and their proprietary CSL language), runtime orchestration (Kubernetes), and deployment tooling. Engineering work touches computer architecture, deep learning kernels, systems software for hardware programmability, and inference serving at scale. Recent partnerships include work with OpenAI on inference deployment, alongside engagements with national laboratories, global enterprises, and healthcare systems requiring high-throughput ML serving.
Cerebras positions its hardware for both training and inference workloads, with claimed industry-leading speeds stemming from on-chip interconnect bandwidth and elimination of multi-chip communication latency. The architecture trades traditional data center modularity for integrated performance - relevant for workloads bottlenecked by cross-device synchronization or where cost-per-inference and tail latency matter more than incremental horizontal scaling. Development infrastructure includes C++, Python, Go, and Zig across the stack, with CI/CD through GitHub Actions and Jenkins.