HiveBear

The world's largest peer-to-peer AI network.
Every device is a node. Every node makes the network smarter.

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Why HiveBear Exists

There are billions of devices sitting idle right now — laptops, desktops, gaming PCs, workstations, even Raspberry Pis — with CPUs and GPUs doing nothing. Meanwhile, running AI costs a fortune in cloud compute, and access is controlled by a handful of companies.

HiveBear connects these idle devices into a single distributed AI network. When you join the mesh, your hardware contributes to a collective compute pool. When you need to run a model that's too large for your machine, the mesh splits it across multiple devices automatically. No central server. No cloud bill. No data leaving the network.

The goal is simple: build a global P2P mesh where anyone can run any AI model, regardless of what hardware they own, by pooling compute with everyone else.

How It Works

   You (8GB laptop)          Friend (16GB desktop)        Mesh peer (GPU workstation)
        |                           |                              |
        +------------- QUIC/TLS encrypted mesh ---------------+
                                    |
                          HiveBear Mesh Network
                                    |
                     Distributed inference: 70B model
                     split across all three devices

1. Install HiveBear on any device
2. Join the mesh — your device auto-profiles its hardware and advertises its capabilities
3. Run any model — if it fits locally, it runs locally. If it doesn't, HiveBear distributes the model layers across mesh peers automatically
4. Contribute idle compute — when you're not using your device, it helps others run their models

# Join the global mesh
hivebear mesh start

# Run a 70B model you couldn't run alone
hivebear mesh run llama-3.1-70b --prompt "Explain quantum computing"

# See who's connected
hivebear mesh status

The mesh uses QUIC transport with TLS encryption. Inference is distributed using pipeline parallelism — each device holds a subset of model layers and forwards activations to the next peer. No raw model weights or user prompts are exposed to other nodes.

It Also Works Standalone

Even without the mesh, HiveBear is a complete local AI runtime. It profiles your hardware, picks the best model and quantization automatically, and runs it:

# One command: profile hardware, pick best model, download, chat
hivebear quickstart

# Or use it as an Ollama-compatible API server
hivebear serve

The serve command is a drop-in replacement for ollama serve — same port (11434), same API. Your existing tools, IDE extensions (Continue, Cody), and scripts work without changes. When a model is too large for your hardware, it automatically overflows to the mesh.

Install

# One-line install (Linux/macOS)
curl -fsSL https://raw.githubusercontent.com/BeckhamLabsLLC/HiveBear/main/install.sh | bash

# Homebrew
brew install BeckhamLabsLLC/hivebear/hivebear

# Scoop (Windows)
scoop bucket add hivebear https://github.com/BeckhamLabsLLC/scoop-hivebear
scoop install hivebear

# Docker
docker run -it --rm -p 11434:11434 ghcr.io/beckhamlabsllc/hivebear quickstart

# Docker with NVIDIA GPU
docker run -it --rm --gpus all -p 11434:11434 ghcr.io/beckhamlabsllc/hivebear:latest-cuda quickstart

# Build from source
cargo install --git https://github.com/BeckhamLabsLLC/HiveBear hivebear-cli

What Your Hardware Can Run

HiveBear auto-detects and adapts to whatever you have:

Device	RAM	Solo	With Mesh
Raspberry Pi 5	8 GB	TinyLlama 1.1B, Phi-2 2.7B	Contribute layers to larger models
Old laptop	8 GB	Llama 3.1 8B (Q4), Mistral 7B	Help run 13B-30B models
Gaming PC	16 GB	Llama 3.1 8B (Q8), CodeLlama 13B	Help run 70B+ models
Workstation	32+ GB	Llama 3.1 70B (Q4), Mixtral 8x7B	Run anything

GPU acceleration is automatic (CUDA, Metal, Vulkan, WebGPU).

Architecture

Rust workspace, 8 crates:

hivebear-core          Hardware profiling, model recommendations
hivebear-inference     Multi-engine inference (llama.cpp, Candle)
hivebear-mesh          P2P distributed inference over QUIC/TLS
hivebear-registry      Model search, download, conversion (HuggingFace)
hivebear-persistence   Conversation history (SQLite)
hivebear-cli           CLI + API server (Ollama + OpenAI compatible)
hivebear-web           WASM bridge for browser inference
apps/desktop           Tauri desktop app (Rust + React)

CLI Reference

hivebear quickstart                    Profile -> recommend -> install -> chat
hivebear serve                         Start Ollama + OpenAI compatible API server
hivebear profile                       Show hardware capabilities
hivebear recommend                     Get model recommendations for your hardware

hivebear mesh start [--port 7878]      Join the P2P mesh network
hivebear mesh status                   Show connected peers and network capacity
hivebear mesh run <model>              Distributed inference across the mesh
hivebear mesh stop                     Leave the mesh

hivebear search <query>                Search models on HuggingFace
hivebear install <model>               Download a model
hivebear run <model>                   Local inference (chat, --api, or --prompt)
hivebear list / remove / storage       Manage installed models

Platforms

• CLI: Linux, macOS, Windows, ARM (Raspberry Pi, Apple Silicon)
• Desktop app: Linux (.deb, .AppImage), macOS (.dmg), Windows (.msi, .exe)
• Mobile: Android (.apk)
• Browser: WASM + WebGPU
• Docker: CPU and CUDA images

Contributing

See CONTRIBUTING.md. The most impactful contributions right now are around the mesh networking layer and hardware profiling coverage.

License

MIT. See LICENSE.