zymtrace.comCrawled Nov 1, 2026
Get code-level insight into CPU performance bottlenecks in production. Identify inefficient methods and optimize cloud costs with continuous, always-on profiling across all major programming languages.
Monitor GPU performance, utilization, memory usage, and hardware health across your cluster. Real-time GPU metrics with process-level breakdowns and seamless integration with GPU profiling.
Zymtrace GPU profiling provides always-on CUDA kernel analysis, SASS instruction profiling, and CPU-GPU correlation. Maximize throughput per GPU per watt with zero-friction deployment for PyTorch, JAX, and CUDA workloads.
Asaf is backing zystem, the team behind Zymtrace, as an investor and advisor.
Blog posts from the Zymtrace team
Anam builds interactive avatars that generate photorealistic video within latency budgets measured in hundreds of milliseconds. By continuously profiling GPU workloads with Zymtrace, they achieved 250% improvement in inference latency and 90% increase in throughput.
Zymtrace adds allocation profiling for Java programs, including Zing-based JVMs. The blog post explains Java memory allocation, Zymtrace’s implementation, and a comparison with async-profiler.
Zymtrace announces $12.2 million in total funding, including an $8.5 million seed round led by Venture Guides. The announcement covers the company’s origins, its AI infrastructure optimization platform, and future plans.
This role sits at the intersection of systems engineering and data science.
Serve as the technical bridge between customers and product, helping organizations optimize their AI infrastructure while shaping the future of GPU performance tooling.
Zymtrace seeks a Forward Deployed Engineer to support customer adoption of its AI infrastructure optimization platform and maximize its value for key clients.
The page compares Zymtrace’s profiling tool with other eBPF-based tools.
Profile-guided, agentic optimization for AI workloads. Zymtrace pinpoints inefficiencies from CPU code to GPU instructions. Self-hosted, cluster-wide, multi-silicon.