Setup and Deployment

Edited by Lawrence Beckwith on February 22, 2026 at 4:44 AM UTC

System Requirements

OtherU Core runs on a single Linux host with sufficient unified or discrete GPU memory to hold your chosen model stack.

Recommended:

  • AMD Ryzen AI Max+ (Strix Halo) with 128GB unified memory, or equivalent
  • Linux with ROCm (AMD GPU) or CUDA (NVIDIA GPU)
  • Docker + Docker Compose
  • 200GB+ storage for models

Minimum (reduced model set):

  • 32GB+ GPU/unified memory
  • Linux with Docker + Compose

Repository Layout

otheru-core/
├── core/coordinator/          # FastAPI orchestrator (bind-mounted into container)
│   ├── routes/openai_api.py   # OpenAI-compatible API handler
│   ├── council.py             # Multi-tier routing
│   ├── routing.py             # needs_tools() and routing decisions
│   ├── agent_chat.py          # preroute_by_pattern(), orchestrator loop
│   └── tools_impl.py          # All tool implementations
├── core/config/
│   └── routing_policy.json    # Routing weights, tool intent keywords
├── services/hardware_bridge/
│   └── bridge.py              # JetKVM WebRTC bridge
└── infrastructure/docker/
    └── docker-compose.production.yml

Models

Place GGUF models in /opt/models/ and HuggingFace models in /opt/models/vllm/.

Role Recommended Model Size
Responder (DEFAULT) Qwen3-Next-80B-A3B Q4_K_M 46GB
Orchestrator (FAST/routing) Nemotron-Orchestrator-8B Q6_K 6.3GB
Reasoner (REASONING) DeepSeek-R1-Distill-Qwen-14B Q4_K_M 8.4GB
Coder Any strong 32B+ code model \~50GB
Vision (Fara) microsoft/Fara-7B 16GB

Deployment

git clone https://git.otheru.ai/otheru/otheru-core
cd otheru-core
docker compose -f infrastructure/docker/docker-compose.production.yml up -d

Service startup order

  1. Core dependencies — Redis, LEANN (RAG)
  2. Orchestrator — Nemotron-8B (tool routing)
  3. Responder — 80B MoE model (primary responses)
  4. Reasoner — 14B R1 model (deep analysis)
  5. Coordinator — FastAPI gateway (depends on all agents)
  6. Hardware Bridge — JetKVM WebRTC bridge (requires physical JetKVM)
  7. GSD — Autonomous dev loop (optional)

Validation Checklist

  • [ ] curl http://127.0.0.1:8080/health{"status":"ok"}
  • [ ] curl http://127.0.0.1:8080/dependencies/health → all services healthy
  • [ ] curl http://127.0.0.1:8005/statsframe_age < 100ms (JetKVM live)
  • [ ] docker logs otheru-coordinator shows models loaded and routing ready

OpenClaw Integration

OtherU Core exposes an OpenAI-compatible API. Connect OpenClaw as a custom provider:

  • Base URL: http://127.0.0.1:8080/v1
  • API Key: local-dev-key (or configure your own)
  • Models: otheru-core, otheru-council, otheru-coder, otheru-researcher

OpenClaw handles Telegram, Signal, and WhatsApp channels out of the box.

Patching the Coordinator

Coordinator source files are bind-mounted from the host — changes take effect on restart:

# Edit on host, then:
docker restart otheru-coordinator

The hardware bridge is baked into its image. Patch it via docker cp:

docker cp services/hardware_bridge/bridge.py hardware-bridge:/app/bridge.py
docker restart hardware-bridge

WMMA Ops Build (AMD gfx1151)

# Container build (recommended)
docker compose -f docker/docker-compose.benchmark.yml build
docker compose -f docker/docker-compose.benchmark.yml run --rm benchmark bash -c \
  "cd /workspace/wmma_ops && ./build_and_test.sh"

# Host build
pip install -e . --no-build-isolation

Validation gates:

  • Correctness checks across representative matrix sizes
  • Benchmark pass for adaptive and zerocopy kernel variants (target: 21.6 TFLOPS on gfx1151)
  • Regression check against baseline PyTorch matmul