We were part of the team that pioneered, open-sourced and donated the eBPF profiler to OpenTelemetry, now in use at Google, IBM, Datadog, Grafana, and more. At Zymtrace, we are bringing that same low-level engineering excellence to GPUs and accelerated AI workloads.
At Zymtrace, our mission is to maximize intelligence per watt, per dollar. AI output is now bounded by power and the supply of advanced chips, and the fastest way to get more of it is not more hardware. It is making the hardware already deployed do more.
That is harder than it sounds. Companies invest billions in compute, yet much of it sits idle or underutilized. The limiting factor is rarely the silicon. It’s the software running on it, and tooling simply hasn’t caught up. Multi-silicon deployments push the complexity even further, with workloads like prefill/decode disaggregation spanning heterogeneous accelerators.
Zymtrace is the performance intelligence layer for multi-silicon AI infrastructure. Always-on in production, with no instrumentation and minimal overhead, it introspects the full stack: from Python and native code through CUDA, ROCm, and XLA, down to the kernels, instructions, and memory behavior that determine efficiency. That context powers profile-guided, agentic optimization of training and inference workloads. Zymtrace finds the bottlenecks, and engineers and AI coding agents consume its recommendations via MCP to close the loop.
Join us, and let’s accelerate the world’s transition to cost-efficient and sustainable computing.
