Compute Layer
GPU rendering, AI batch workloads and future worker nodes routed by priority, availability and energy conditions.
Energy-aware compute lab
More useful work per watt.
LazyHash is an early-stage lab for energy-aware compute — combining GPU workloads, Bitcoin mining efficiency, solar energy, batteries and adaptive scheduling.
Solar
Battery
Grid
↓
Energy Layer
price · state · priority · capacity
↓
GPU Render
AI Batch
ASIC Mining
What is LazyHash?
Compute should not run blindly.
LazyHash explores how hardware can do more useful work with less waste: render jobs when capacity is available, mine only when it makes sense, buffer workloads with batteries, and adapt power use to real-world conditions.
First commercial project
GPU Render Studio
GPU Render Studio is our first focused compute service, built for Blender rendering and GPU workloads. LazyHash develops the broader efficiency layer behind scheduling, benchmarks and energy-aware operation.
Project tracks
One system, several layers.
Mining Layer
Bitcoin mining efficiency, ASIC undervolting, immersion cooling and flexible-load experiments.
Energy Layer
Solar-aware compute, battery-buffered workloads, electricity price logic and adaptive power control.
LazyHash Lab
Benchmarks, architecture notes, experiments and a public roadmap for what is being built next.
Energy Layer
Compute should follow energy.
The Energy Layer connects solar production, battery storage, electricity pricing and workload scheduling.
The goal is to run flexible workloads when energy is available, cheap or strategically useful — and pause, delay or undervolt them when it is not.
Solar production
Battery state
Electricity price
Grid limits
↓
Run · Delay · Undervolt · Pause
↓
GPU · AI · ASIC workloads
Lab notes
Early notes, benchmarks and architecture drafts.
RTX 5090 Render Efficiency
Measuring render time, power limits and energy per completed GPU job.
Energy-Aware Scheduling
How LazyHash decides when compute should run, wait, undervolt or pause.
Solar + Battery Buffered Compute
Exploring how flexible workloads can follow solar production, battery state and electricity price.
ASIC Undervolting Notes
Practical experiments in lower J/TH mining, immersion cooling and older ASIC reuse.
Roadmap
Early, practical and intentionally transparent.
Active
- LazyHash landing page
- GPU Render Studio starter
- Compute Layer structure
- Energy Layer concept
Testing
- RTX render efficiency benchmarks
- ASIC undervolting
- Workload scheduling logic
- Cheap queue pricing model
Future
- Trusted worker nodes
- AI batch compute
- Battery-buffered operation
- Mining pool experiments
Follow the build
LazyHash is early.
Follow the lab as we test GPU rendering, mining efficiency, energy-aware scheduling and battery-buffered compute.