Any workload
CPU, GPU, or mixed. Containerized jobs. Long-running or batch. The network handles scheduling and fault tolerance.
Verifiable
Every job produces a cryptographic receipt. Output hashes are signed and published. Audit any result.
Burst capacity
Spike to thousands of nodes when you need them. Pay only for what you use. No commitments.
GPU support
H100, A100, MI300X. Multi-GPU jobs. Distributed training across nodes.
Petabyte storage
Mount distributed storage to your jobs. Stream inputs and outputs. Pay per GB.
Pay in QUBIC
No credit card. Stake to access compute at a discount. Burn on idle. Earn on usage.
Use cases
If you can containerize it, you can run it on Aigarth.
Scientific simulation
Molecular dynamics, climate models, CFD, genomics.
Financial modeling
Monte Carlo, risk sims, backtesting at scale.
Render farms
Animation, VFX, architectural visualization.
Batch ETL
Process terabytes nightly. Cheaper than reserved cloud.
Distributed training
Multi-node model training. Horovod, DeepSpeed, FSDP.
Image & video processing
Transcoding, batch processing, watermark application.
Architecture
Submit a job. The scheduler finds the lowest-cost, lowest-latency workers. Outputs are signed and replicated.
# Submit a job to the network
import aigarth
client = aigarth.Client(api_key="sk-...")
job = client.compute.submit(
image="docker.io/myorg/sim:latest",
command=["./run", "--scale", "1000"],
gpu="H100",
replicas=64,
timeout="6h",
)
# Poll for completion
result = job.wait()
print(f"Job complete: {result.output_hash}")