BASHCAT
Self-hosted · open beta · v0.8.3

LLM Cluster

Distributed GPU sharing

String idle GPUs into your own compute pool. Team-shared scheduling, priority queues, transparent costs — like Slurm, but for the LLM era.

Request accessGitHub →
beta · v0.8.3
LLM Cluster
$ cluster status
nodes: 4 online · 12× A6000 · 8× 4090
queue: 3 pending · 2 running · 18 done
$ cluster submit train.yml
job-471 → node-2 (RTX A6000)
eta 1h 24m · cost est $1.18
deploySelf-hosted (k8s)
GPUNVIDIA · CUDA 12+
schedpriority + fair-share
licenseopen core
01How it works

Idle GPUs → shared compute pool.

01

Register nodes

Install agent on any GPU box — desktop, server, or cloud. Auto-detects.

02

Submit jobs

YAML config or one-line CLI. Choose GPU class, deadline, priority.

03

Monitor + bill

Live dashboard, per-user cost, exportable usage reports.

02Features

Built for teams, not single users.

01

GPU scheduling

priority + fair-share

Mix priority queues (urgent inference) with fair-share (training jobs).

02

Job queue

durable

Crash-safe queue. Re-queue on node failure. Retry policies per job.

03

Live dashboard

metrics

GPU utilization, VRAM, queue depth, job timeline — all in one view.

04

Cost tracking

per-user / per-team

GPU-hour accounting per user, team, or project. Export to CSV.

05

Multi-tenant

isolation

Namespace isolation, RBAC, per-team resource quotas.

06

Open core

self-host

Core is Apache 2.0. Enterprise add-ons (SSO, audit, SLA) available.

Got something to build?
Let's talk.

First 30-min tech consult is on us. No NDA needed for the kick-off chat — that comes later.