What is Magpie?#
2026-07-22
2 min read time
Magpie is a lightweight, general-purpose framework for evaluating GPU kernel correctness and performance. It provides a single, reproducible workflow for checking that a kernel is correct, comparing competing implementations, and benchmarking full inference frameworks, on both AMD (HIP/ROCm) and NVIDIA (CUDA) hardware.
Magpie is a component of the Hyperloom toolkit (AMD’s multi-tool GPU evaluation platform), but can also be used as a standalone tool. The Magpie source code is hosted in the AMD-AGI/Magpie GitHub repository.
What Magpie does#
Magpie organizes kernel evaluation into three modes:
Analyze – Evaluate a single kernel against a testcase for correctness, then optionally profile its performance.
Compare – Evaluate and rank multiple kernel implementations against a baseline to find the fastest correct variant.
Benchmark – Run framework-level benchmarks (vLLM, SGLang, Atom) with optional torch and system profiling, including TraceLens trace analysis and kernel-level gap analysis.
Key features#
Magpie provides the following capabilities:
Three evaluation modes: analyze, compare, and benchmark.
Heterogeneous hardware: AMD (HIP) and NVIDIA (CUDA) GPUs.
Multiple execution environments: local host, sandboxed container, and remote Ray cluster.
Hardware-aware evaluation: controlled execution with optional power and frequency settings.
Automatic GPU selection: benchmark mode picks idle GPUs before launching.
Trace analysis: TraceLens integration for performance profiling and gap analysis.
MCP server: Model Context Protocol integration for AI agents.
Structured reports: JSON output for pipeline integration.
Use cases#
Validate hand-written or AI-generated GPU kernels for correctness before promoting them.
Rank competing kernel implementations to pick the fastest correct one.
Benchmark and profile LLM inference frameworks on AMD GPUs and locate the kernels that dominate runtime.
Get started with Magpie#
To get started with Magpie, see the following pages.
Install Magpie: Get up and running in minutes.
Analyze and compare kernels: Validate a kernel and rank competing implementations.
Benchmark frameworks: Run vLLM, SGLang, or Atom benchmarks with trace analysis.
MCP server and agent skills: Drive Magpie from an AI agent.