Ferrum is an optional local model server for this example, not an Orchestral dependency. Orchestral also connects to other OpenAI-compatible providers.

On macOS (Apple Silicon) or Linux (x86_64), install the latest releases:

curl -fsSL https://ferrum.pandaailabs.com/install.sh | sh
curl -fsSL https://orch.pandaailabs.com/install.sh | sh

On Windows (x86_64), use PowerShell:

irm https://ferrum.pandaailabs.com/install.ps1 | iex
irm https://orch.pandaailabs.com/install.ps1 | iex

Open a new terminal after installation, then start Ferrum:

ferrum serve --model unsloth/Qwen3.5-9B-GGUF

Ferrum automatically selects an available backend and downloads the model and metadata as needed, reusing its cache on later starts. Wait for the server to be ready, then open another terminal in your project directory and run:

orchestral --base-url http://127.0.0.1:8000/v1 --no-auth

Type a task and press Enter. These defaults are a quick starting point, not a reproduction of the recording's three-session configuration or performance.

The recording uses an M1 Max Mac with 32 GB unified memory, Metal, and Qwen3.5-9B Q4_K_M. The commands below reproduce its serving settings: 24,576 tokens per context, three active sequences, a 20 GiB runtime memory budget, and the model's default thinking behavior. Use Ferrum 0.10.0 and Orchestral 0.3.1. No JSON configuration or API key is required.

Install both programs once, then open four terminal panes:

curl -fsSL https://ferrum.pandaailabs.com/install.sh | sh -s -- --version 0.10.0 --backend metal
curl -fsSL https://orch.pandaailabs.com/install.sh | sh -s -- --version 0.3.1
export PATH="$HOME/.local/bin:$PATH"
ferrum --version
orchestral --version

Terminal 1 — upper left: start Ferrum. The first start downloads the selected GGUF and its model/tokenizer metadata from Hugging Face; subsequent starts reuse the cache. The repository revision and filename select the weights used in the video.

ferrum serve \
  --model unsloth/Qwen3.5-9B-GGUF@3885219b6810b007914f3a7950a8d1b469d598a5 \
  --gguf-file Qwen3.5-9B-Q4_K_M.gguf \
  --served-model-name Qwen3.5-9B \
  --backend metal \
  --numerical-profile qwen3_5.f32-master \
  --host 127.0.0.1 --port 8001 \
  --max-model-len 24576 \
  --max-num-seqs 3 \
  --max-num-batched-tokens 3072 \
  --scheduler-prefill-step-chunk 1024 \
  --scheduler-active-decode-prefill-chunk 256 \
  --enable-prefix-cache \
  --runtime-memory-budget-bytes 21474836480 \
  --prefix-rendezvous-max-wait-ms 180000

Leave Ferrum running. In another terminal, check that it is ready before starting the agents. This discovers the served model without generating a response:

orchestral --base-url http://127.0.0.1:8001/v1 --no-auth doctor --check-connection

Terminal 2 — upper right: replace the path with your first project directory.

cd /path/to/project-a
orchestral --base-url http://127.0.0.1:8001/v1 --no-auth

Terminal 3 — lower left: open your second project.

cd /path/to/project-b
orchestral --base-url http://127.0.0.1:8001/v1 --no-auth

Terminal 4 — lower right: open your third project.

cd /path/to/project-c
orchestral --base-url http://127.0.0.1:8001/v1 --no-auth

Type a task in each Orchestral terminal and press Enter. Each session uses the same Ferrum server. The video uses three separate Rust projects with Cargo installed, and asks each agent to fix failing tests, preserve the public API, run cargo test, and explain the fix in English.