Skip to main content

Quick Start

What you'll get

Deploy in minutes. No code changes. zymtrace immediately shows you what is hurting performance across GPU and CPU workloads, down to the exact function, kernel, and stall reason — with AI-powered fixes and MCP access from your IDE.

vLLM GPT-OSS inference flamegraph with AI assistant recommendations

This is a GPT-OSS inference run on vLLM. The flamegraph on the left shows the full call path from Python down to CUDA kernels. The AI assistant on the right identified that hal.matmul is consuming ~50% of GPU time due to suboptimal kernel tiling, and that _prepare_inputs is stalling the GPU with synchronous host-to-device copies on every forward pass. Exact fixes for both are surfaced automatically.

Get More Value – Let's Talk!

There's a good chance you could unlock more value from zymtrace, and do it faster by having a quick chat with us about your use cases. We're a team of technical founders, not your typical pushy salespeople, and there's probably a thing or two we can learn from each other.

Reach out to us here and we’ll set you up with an advanced trial licence.

You're also welcome to continue with the self-guided trial below.

Get Started in Just 3 Simple Steps!​

zymtrace is shipped with a free perpetual trial license — use it at no cost.

Step 1: Add the zymtrace Helm repository​

Artifact Hub

Helm Chart Source

The Helm chart source code is available on GitHub: zystem-io/zymtrace-charts

To get started, add the zymtrace Helm repository by running the following command:

helm repo add zymtrace https://helm.zystem.io

To verify and list available charts and versions, use:

helm search repo zymtrace --versions

Step 2: Install the backend components​

Deploy the backend components using Helm:

helm upgrade --install backend zymtrace/backend \
  --namespace zymtrace \
  --create-namespace \
  --reset-then-reuse-values \
  --debug \
  --atomic

Step 3: Install the profiler as a DaemonSet​

tip

The collection-agent arg should point to the gateway service or ingress, if you exposed it.

Deploy the profiler as a DaemonSet using the following command:

GPU Profiling enabled:

helm install profiler zymtrace/profiler \
  --namespace zymtrace \
  --create-namespace \
  --set profiler.cudaProfiler.enabled=true \
  --set profiler.args[0]="--collection-agent=zymtrace-gateway.zymtrace.svc.cluster.local:80" \
  --set profiler.args[1]="--disable-tls" \
  --set profiler.args[2]="--enable-gpu-metrics" \
  --set profiler.args[3]="--nvml-auto-scan"

Note: Use --nvml-path=/path/to/libnvidia-ml.so instead of --nvml-auto-scan if you know the exact NVML library path.

For a step-by-step guide to GPU profiling with Binary, Docker, or Kubernetes, see the GPU Profiler Quick Start. For advanced configuration, see Profiling CUDA Applications.

For CPU Profiling only:

helm install profiler zymtrace/profiler \
  --namespace zymtrace \
  --create-namespace \
  --debug \
  --set profiler.args[0]="--collection-agent=zymtrace-gateway.zymtrace.svc.cluster.local:80" \
  --set profiler.args[1]="--disable-tls"

We also have arm64 and x86/amd64 binary distributions of the zymtrace profiler.

Start Using zymtrace: Efficiency IQ​

Once deployed, access the Efficiency IQ page for a high-level overview of your fleet's performance. From this view, you can quickly spot performance spikes and identify emerging usage patterns, allowing you to optimize your system effectively. You can also navigate to flamegraphs and functions page from the Efficiency IQ dashboard.

You're all set!

📐 Architecture

Learn about zymtrace's architecture and components

Learn More

✨ Install zymtrace Backend

Deploy zymtrace backend

Learn More

⚡ Install zymtrace Profiler

Install zymtrace agent with Helm, Kubernetes manifests, Docker...

Learn More