Loading...
Loading...
Access powerful GPU clusters on-demand. Bittensor-validated quality. Per-second billing. 60% cheaper than hyperscalers.
A complete platform for running AI workloads at scale.
Drop-in replacement for OpenAI, Anthropic, and Cohere endpoints. Zero code changes — just swap your base URL.
Every provider is continuously benchmarked by Bittensor validators. Underperforming hardware is automatically deprioritized.
SSH directly into dedicated GPU instances with root access. Install custom libraries, run arbitrary CUDA code, attach persistent storage.
Billed with per-second granularity. Run a 47-second test and pay for exactly 47 seconds. No rounding, no minimums.
All inter-node traffic runs through WireGuard encrypted tunnels. Jobs execute in isolated Docker containers with zero shared GPU memory.
If a provider node drops mid-job, your workload migrates to an equivalent GPU with checkpoint recovery. Zero manual intervention.
from vexnode import VexNode
client = VexNode(api_key="your-key")
job = client.compute.create(
model="meta-llama/Llama-3-70B",
gpu_type="A100",
gpu_count=4,
script="train.py",
dataset="s3://my-bucket/data",
)
print(f"Job {job.id} running on {job.gpu_count}x {job.gpu_type}")Native support for the frameworks you already use.
Join the waitlist and get early access to VexNode GPU compute.