eBPF-Based AI Workload Profiler for GPU Kubernetes Clusters
Pixie pioneered eBPF observability for Kubernetes, but GPU workloads (LLM inference, training) are invisible to existing tools. A specialized eBPF profiler for AI workloads that captures GPU utilization, model inference latency, batch efficiency, and memory pressure without code changes could help AI platform teams optimize expensive GPU infrastructure.
Problem Statement
AI platform teams running LLM inference and training on Kubernetes cannot easily see per-model GPU utilization, batch efficiency, inference p99 latency, or memory fragmentation without modifying application code. NVIDIA DCGM provides raw GPU metrics but not application-level context. Teams over-provision GPUs because they lack visibility into actual usage patterns.
The Idea
An eBPF-based observability tool specialized for AI/ML workloads on Kubernetes that profiles GPU utilization, inference latency, batch efficiency, and memory pressure without application code changes.
Why Now
GPU costs dominate AI infrastructure budgets (60-80% of total spend). KubeCon EU 2026 featured multiple sessions on LLM workload observability gaps. Existing tools (Prometheus, Grafana) require manual instrumentation and miss GPU-specific metrics. eBPF enables zero-instrumentation profiling that AI platform teams need to optimize million-dollar GPU clusters.
Target User
AI platform engineers and MLOps teams managing GPU Kubernetes clusters for inference and training
Target Market
Organizations running AI workloads on Kubernetes with $50K-5M monthly GPU infrastructure budgets
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