GPU Compute

Run or serve GPU workloads from one command

Badgr supports many GPU workloads through two commands: badgr serve for persistent endpoints, badgr run for jobs. You don't configure compute providers. You run commands.

1. Install and log in

npm install -g badgr-cli

Requires Node.js 18+. Installs the badgr command.

badgr login

Prompts for your API key and saves it to ~/.badgr/config.json.

2. Two commands, everything else is a recipe

badgr serve <model>Start a persistent OpenAI-compatible endpoint. Stays running until you run badgr down.
badgr run <command>Run a one-off GPU job. Streams logs, exits when done. Billing stops automatically.
badgr down <id>Terminate any deployment. Stops billing immediately.
badgr logs <id>Fetch log output from a running or completed deployment.
badgr receiptsCost, route, and retry record for every action.

3. What you can build

badgr serve— persistent endpoints

Serve an open-source model

LLaMA, Mistral, Qwen, and any Hugging Face model via vLLM

Deploy a vLLM endpoint

Custom vLLM config, version pinning, extended context

Embeddings endpoint

Run TEI or a vLLM embedding model as a persistent API

Image generation API

Serve a Diffusers or ComfyUI container as an endpoint

Transcription API

Persistent Whisper endpoint for audio-to-text workloads

badgr run— jobs that start, run, and exit

One-off GPU job

Run any Python script or container command on a GPU

Batch inference

Offline scoring, embedding generation, large-scale eval

Fine-tuning / LoRA

Adapter training with Axolotl, TRL, or custom scripts

Image and video batch jobs

Diffusers, ComfyUI, or video synthesis pipelines

Audio and transcription jobs

Whisper batch jobs, audio processing pipelines

Integrations

GitHub Actions

badgr-run and badgr-serve composite actions for CI/CD pipelines

MCP (agent compute)

badgr-mcp exposes GPU tools to Claude, Cursor, and other coding agents

4. Quick examples

Serve a model

badgr serve meta-llama/Llama-3.1-8B-Instruct

Run a job

badgr run python train.py --gpu A100

Stop billing

badgr down <deployment-id>

GPU options

Badgr Auto selects the best eligible GPU for your workload. Use --gpu or --min-vram only when you need more control.

Flag valueGPUVRAM
RTX_3090NVIDIA RTX 309024 GB
RTX_4090NVIDIA RTX 409024 GB
L40SNVIDIA L40S48 GB
A100NVIDIA A10040–80 GB
H100NVIDIA H10080 GB

Available GPU types may vary by region and current capacity. Run badgr capacity or use --dry-run to confirm availability and pricing before provisioning.

Coming soon

Real-time log streaming (currently polls every 4 s — functional, not true streaming)
Multi-node training jobs (distributed across multiple GPU machines)
Spot/preemptible GPU instances for non-critical batch workloads

Not supported

AMD Radeon GPUs
Intel Arc GPUs
Apple Silicon (M-series)
NVIDIA T4 / V100 (retired from pool)
Consumer GPUs below RTX 3080 (insufficient VRAM for most workloads)