Skip to main content

nvHive

One curl command turns a rented Linux GPU desktop — GeForce NOW, RunPod, Lambda, Vast — into a working AI lab. No root, no Docker, survives reconnects.

PyPI License: PolyForm NC CI

curl -sSL https://raw.githubusercontent.com/thatcooperguy/nvHive/main/install.sh | bash

That one command gives you:

  • A local multimodal LLM, auto-picked for your GPU's VRAM, served by Ollama — chat and image understanding with nothing leaving the machine
  • A web dashboard at localhost:3000 with an AI Wizard that knows your workspace and can fix it
  • A multi-LLM router across 23 providers (Ollama, Groq, Gemini, NVIDIA NIM, OpenAI, Anthropic, ...) — many with free tiers
  • Persistent storage layout under NVH_HOME, so models and chats survive when the cloud desktop resets

Everything installs to user-owned paths on the persistent volume, and the browser only opens after every service passes a health check. You pay for GPU time, not debugging time.


What happens when you run it

Three visible steps — nothing hidden, everything skippable:

1. Model download countdown. You're told what's downloading, how big it is, and how to skip:

AI Wizard local brain: llama3.2-vision (~7.9 GB)
  This is the model the Wizard chats with. Smaller models load fast on CPU;
  bigger ones are stronger on GPU. You can change it later from the WebUI.
  Starting in 10s... press [s] to skip

Models are picked by VRAM: moondream (~1.7 GB, runs on CPU) → minicpm-v (12 GB+) → llama3.2-vision (16 GB+) → NVIDIA nemotron-3-nano-omni (24 GB+) → nemotron-omni (40 GB+). If a pull fails, the installer falls through to the next smaller model instead of dying. Headless installs can opt out with NVH_INSTALL_MODEL_DOWNLOAD=0.

2. Verified bring-up. Services start in dependency order with real health gates, shown live:

                       nvHive bring-up
 Service                    Port    Status     Detail
 Local AI brain (Ollama)    11434   ✓ ready    /api/tags responding
 nvHive backend (API)       8000    ✓ ready    /v1/health ok
 Web dashboard (WebUI)      3000    ✓ ready    serving
 End-to-end test            —       ✓ ready    Wizard answered

The fourth row is a real smoke test: it POSTs a chat message to the Wizard and waits for an answer.

3. Browser opens only on green. Your first sight of the dashboard is a working dashboard — never a red "API offline" banner. If anything fails, you get the failing step, the log path, and the last 25 lines of that log inline.


What you can do with it

Local AI, immediately

The dashboard's Wizard chat runs against your local model first — $0, private, offline-capable. The CLI works the same way:

nvh "summarize this error log"      # routes to the best available model
nvh safe "review this contract"     # local only — nothing leaves the machine

AI Wizard

A streaming, tool-using assistant that reads live workspace state. It can refresh models, repair the workspace, RAG over files you drag into the chat (PDFs included), and search the web — citing sources and showing cost and latency per response. Attach external MCP tool servers and their tools join the Wizard's toolset too. Slash commands in chat: /help, /save, /pin, /clear, /tools.

Multi-provider routing

One interface over 23 providers and 63 models. Requests are scored on capability, cost, latency, and provider health, then routed — free tiers first when you have no keys, your GPU first when you do have one. Add keys with nvh setup. Provider guide

Model Manager

nvHive detected your GPU at install, so the Models page shows whether each catalog model fits your VRAM and how much disk it needs before you download — then installs it with one click and live progress. Same thing from the terminal:

nvh models list --all               # fit-ranked catalog for your GPU
nvh models pull gemma3:4b           # install with live progress

Model Manager guide

Agents and council mode

An Agent Library of 100+ profiles across 38 categories (coding, research, creative, GPU media, ops, education, …) plus six core built-ins — each mappable to a local or cloud model; your own profiles live in $NVH_HOME/agent-profiles/. Council mode runs one question through multiple models in parallel and synthesizes the answers:

nvh convene "Redis or Postgres for session storage?"   # multi-model deliberation
nvh agent "add unit tests for auth" --dir ./myproject  # agentic coding with QA

Council docs · Agent tools

Creative and studio packs

Rootless one-command installs for ComfyUI, Blender, game-dev tooling, and music production (stem splitting, transcription, generation):

nvh studio --list
nvh studio --install comfy -y
nvh studio --install creative -y
nvh studio --install music -y

Built for machines that disappear

Cloud GPU desktops reset. nvHive plans for it:

  • Everything that matters lives under NVH_HOME on the persistent volume — models, config, chats, vault, logs, jobs
  • Long downloads run as resumable jobs that survive browser refreshes and reconnects
  • /pin a conversation and a Welcome Back panel resumes it on the next session
  • nvh snapshot save tarballs your state; nvh snapshot restore resumes it on a brand-new VM

If your persistent mount isn't auto-detected, set it before installing:

export NVH_HOME=/mnt/persist/nvhive

Requirements

  • Linux x86_64 (the primary target; Windows and macOS installers exist — see Releases)
  • No root. Everything installs to user-owned paths. No Docker required.
  • Python 3.11+ — or none at all: the installer can fetch a single-file binary (NVH_USE_BINARY=1)
  • GPU optional. CPU-only machines get moondream locally plus cloud free tiers. An NVIDIA GPU unlocks the larger local models.
  • Disk: ~2 GB minimum for the smallest local model; up to ~35 GB for the largest tier. The installer checks free space and tells you sizes before downloading.

Already have a Python environment? pip install nvhive (extras: [vision], [browser], [rag], [all]).


When something breaks

Three places to look, in order:

nvh services status        # per-service health table
nvh services smoke-test    # "can the Wizard actually answer?" end-to-end check
nvh doctor                 # full diagnostic

In the dashboard, the Debug Report button generates a redacted report (secrets and local paths stripped) you can paste into an issue. Logs live under $NVH_HOME/logs/ (ollama.log, api-server.log, model-pull.log). nvh services restart recycles the stack; nvh repair runs safe rootless fixes.


Command reference

Command What it does
nvh "question" Route to the best available model
nvh safe "question" Local inference only
nvh convene "question" Multi-model council with synthesis
nvh agent "task" Agentic coding with review loop
nvh webui Open the dashboard
nvh services start Verified bring-up (Ollama → API → WebUI → smoke test)
nvh services stop Stop the stack (keeps Ollama's warm model cache)
nvh studio --install <pack> -y Install a rootless tool pack
nvh snapshot save / restore Persist state across ephemeral VMs
nvh setup Configure providers and keys

Full reference: docs/COMMANDS.md

Documentation

Guide What's inside
Linux GPU Desktop The no-root cloud workstation path in depth
GPU Tier Matrix Which capabilities unlock at which VRAM
Providers All 23 providers, free tiers, rate limits
Council Multi-LLM deliberation design
Architecture Routing, layers, system design
SDK & API Python SDK, REST API, OpenAI/Anthropic-compatible proxies
Configuration Every knob, including NVH_HOME and install env vars

Notes

  • Cloud providers receive the queries you route to them, under their own privacy policies. Use nvh safe to keep inference local.
  • AI output can be wrong. Review agent-modified files before shipping them.

License

PolyForm Noncommercial 1.0.0 — you can use, modify, and share nvHive freely for any noncommercial purpose. Selling this code or using it commercially is not permitted. See LICENSE and NOTICE. (Versions 0.40.0 and earlier were released under MIT and remain MIT.)

The license does not grant rights to the nvHive name or logos; forks should use distinct names and channels. See TRADEMARKS.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nvhive-0.41.0.tar.gz (1.0 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

nvhive-0.41.0-py3-none-any.whl (852.8 kB view details)

Uploaded Python 3

File details

Details for the file nvhive-0.41.0.tar.gz.

File metadata

  • Download URL: nvhive-0.41.0.tar.gz
  • Upload date:
  • Size: 1.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.9

File hashes

Hashes for nvhive-0.41.0.tar.gz
Algorithm Hash digest
SHA256 32b6ea9bb54bbdfa6775fc0129aa2abbb7e3f4dac6282a2e42a2e87400ddbc49
MD5 7901e5f6636bd31a132d516094058cca
BLAKE2b-256 8dcdbada0b9442d0ee703802c7c54f4fd826d5a9516ba4546a603af029cb0831

See more details on using hashes here.

File details

Details for the file nvhive-0.41.0-py3-none-any.whl.

File metadata

  • Download URL: nvhive-0.41.0-py3-none-any.whl
  • Upload date:
  • Size: 852.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.9

File hashes

Hashes for nvhive-0.41.0-py3-none-any.whl
Algorithm Hash digest
SHA256 a9801992d623a3aff54e6da005ed2a46f6449a35eb1f43bd4a4989b5751752bd
MD5 3394c67e846507be7ee29f3ae718fcb6
BLAKE2b-256 6a0f4c44abb88c4ea366160a3e97c2c530d70c4285729c1dfd3426d6183d63b1

See more details on using hashes here.

Release history Release notifications | RSS feed

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page