Idle GPUs, working together

Built on pooled compute. Powered by open-weight models.
Delivering batch AI at campus scale.

Runs open-weight models

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Platform

Everything a batch needs

From upload to download, the pool does the rest.

Batch API

Upload a JSONL of prompts, submit a batch, download results. OpenAI-compatible — existing code migrates by changing one line.

Distributed GPU workers

Jobs route to real GPUs pooled from labs and clusters. Workers pull work, so they run anywhere — even behind campus NAT.

Organizations & teams

A lab is an org. Owner, admin and viewer roles, shared worker keys, and every job attributable to the team that ran it.

Usage analytics

Requests, models, and per-worker contribution tracked from day one — the utilization evidence your institution wants.

Developers

One-line migration

BEFORE
base_url = "https://api.openai.com/v1"
AFTER
base_url = "https://api.sheshnag.io/v1"
api_key = "gk-your_personal_key"
# Everything else stays the same

OpenAI-compatible REST

Files and batches, verbatim. Your SDK already speaks it.

Model catalogue

Pick a pinned model id from /v1/models — copy, paste, run.

Dashboard

Submit, track, and download without touching curl.

Providers

Built for machines that come and go

Pull-based by design

Workers poll for work and disappear without ceremony. Intermittent lab machines are a feature, not a failure.

Fault tolerant

Silent workers are detected in minutes and their jobs requeued automatically. Batches survive the hardware they run on.

Curated model catalogue

Every servable model is a pinned artifact — weights, quantization, runtime. Reproducible by construction.