Zymtrace offers an eBPF‑based continuous profiling platform that provides whole‑system visibility into CPU‑GPU interactions for AI inference and distributed training workloads. By correlating token‑level performance with hardware metrics, it identifies bottlenecks such as AllReduce stalls and memory‑transfer saturation, delivering actionable Efficiency IQ insights that improve throughput, reduce latency, and lower cost‑per‑token on existing GPU infrastructure.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises running AI inference and distributed training often experience hidden performance bottlenecks between CPU and GPU, leading to underutilized hardware, high latency, and elevated compute costs. Without fine‑grained, system‑wide visibility, engineers cannot pinpoint stalls such as AllReduce delays, memory‑transfer saturation, or inefficient kernel usage.
Solution
Zymtrace provides an eBPF‑based continuous profiling platform that captures detailed CPU‑GPU interaction metrics across entire clusters without code changes. The agent runs with a minimal footprint, correlates token‑level inference performance with hardware profiles, and surfaces actionable “Efficiency IQ” insights that guide engineers to specific code paths, kernel configurations, or batch sizes to improve throughput, reduce latency, and lower cost‑per‑token. Recommendations are delivered via a self‑hosted dashboard and can be applied automatically or manually, enabling faster, cheaper, and more sustainable AI workloads on existing GPU infrastructure.
Target Audience
Primary customers are engineering teams responsible for large‑scale AI inference and training deployments—such as ML platform engineers, DevOps, and performance engineers in cloud providers, enterprises, and research institutions.
Features
- Zero‑friction deployment as a Docker, Kubernetes DaemonSet, or binary with no instrumentation required
- Whole‑system visibility for all major languages (Python, Java, Go, Rust, C/C++, etc.) on CPU and GPU workloads
- eBPF‑powered profiling of CPU‑GPU interactions, exposing AllReduce stalls, memory‑transfer saturation, and kernel inefficiencies
- Support for NVIDIA CUDA, PyTorch, JAX, AWS Inferentia and MIG‑aware GPU metrics
- AI‑driven “Efficiency IQ” that pinpoints the most expensive functions and suggests concrete optimizations
- Cluster‑wide, OpenTelemetry‑compliant metrics and alerts with <1% CPU and <250 MB RAM overhead
- Profile‑guided optimization loop that can automatically apply fixes or provide step‑by‑step guidance