Prepare
Reads the system profile and prepares optional assets or acceleration only when they are needed.
Benchmark methodology
Lydian combines focused CPU, GPU, ray tracing, and local AI workloads in a consistent benchmark journey shared by the desktop app and command line.
The shared execution layer
Runner coordinates the benchmark from start to finish. It keeps workload order, progress, cancellation, and result handling aligned across every Lydian experience without changing what each platform is asked to do.
Reads the system profile and prepares optional assets or acceleration only when they are needed.
Runs each workload in a defined order with the same settings and boundaries.
Uses warmups and repeated samples while reporting live progress and respecting cancellation.
Turns raw timings and throughput into normalized component scores and a balanced suite result.
Packages the result with its system context and authenticates public submissions before ranking.
Full benchmark
The full suite moves from focused processor work to parallel compute, rendering, and local inference. Runner carries progress and results between each component.
What is measured
Single + multi-core
Ten practical workloads exercise computation, cryptography, compression, navigation, image processing, text, web, and asset handling.
Parallel throughput
Compute workloads cover matrix math, memory bandwidth, cryptography, transforms, and image processing through the best supported graphics API.
Progressive rendering
A consistent 3D scene is rendered progressively in tiles, putting traversal, shading, and parallel execution under sustained load.
On-device language model
A compact language model runs locally, with required assets prepared on demand and available hardware acceleration selected automatically.
Methodology and trust
A benchmark is only useful when the journey behind the number remains consistent and understandable.
Workloads execute locally, so the result reflects the hardware and software environment being tested.
Desktop and CLI use the same orchestration path, keeping suite order and scoring semantics aligned.
Stages, preparation, and long-running work report progress, and a cancelled run stops cleanly.
Public results are signed with a per-install identity and verified before reaching the leaderboard.
Questions
It is the shared layer that coordinates benchmark execution for the desktop app and CLI. It keeps the public benchmark behavior consistent without being a separate benchmark itself.
The suite combines CPU single-core and multi-core performance, GPU compute, progressive ray tracing, and local AI inference into a broad view of the system.
Each component uses repeatable workloads, representative samples, and normalized scoring. Higher scores indicate more work completed or faster completion under the same benchmark conditions.
Not directly. It measures compute and rendering capabilities that matter to many workloads, but it does not report game frame rates.