Codendum

Codendum documentation

Codendum is shared, local infrastructure for coding agents. A single NVIDIA GB10 system (DGX Spark) runs vLLM with a coding model, and concurrent users run OpenCode on their own workstations against it. It works the same for a classroom or a company team: prompts and source code stay on the organization’s network.

These pages explain how to install, secure, test, measure and operate the service. They follow the order of a first installation.

How it works

 User workstations                       GB10 host (DGX OS, ARM64)
 ┌──────────────────────────┐            ┌───────────────────────────────────────────┐
 │ OpenCode                 │  HTTPS     │ nginx container :8443                     │
 │ Git, toolchains, IDEs,   │───────────▶│  network allowlist, per-user API key,     │
 │ builds and tests         │  /v1/...   │  per-user limits, only 2 endpoints        │
 └──────────────────────────┘  LAN/VPN   │        │                                  │
                                         │        ▼ 127.0.0.1:8000                   │
                                         │ vLLM container (served model "coder")     │
                                         │ Qwen3-Coder-30B-A3B-Instruct-FP8          │
                                         └───────────────────────────────────────────┘

  • The GB10 only serves inference. Users’ code, version control, compilers, interpreters, package managers, builds and tests run on the workstations or in dedicated isolated development environments, whatever the language or platform. Do not run users’ builds on the model host: CPU and GPU share the same memory.

  • Every service runs in Docker. vLLM listens on the loopback interface only, and an nginx container is the only way in. nginx accepts HTTPS on a dedicated port from the organization’s LAN or VPN, checks a per-user API key and forwards only /v1/chat/completions and /v1/models. The scripts in this repository run on the host: they drive Docker and act as test clients, and they install nothing on the system.

  • OpenCode uses an explicit OpenAI-compatible provider. The served model name is coder, tool calling is enabled explicitly, and the context limit matches the server profile.

Path

Purpose

scripts/preflight.sh

Read-only host checks (ARM64, Docker, GPU runtime, memory, disk, ports)

scripts/start-vllm.sh

Starts the vLLM container with the selected profile

scripts/start-proxy.sh

Starts, checks and reloads the nginx proxy container

scripts/smoke-test.sh

Health, model list, chat, streaming and tool-call checks

scripts/metrics.sh

KV cache, running/waiting requests, preemptions, host memory, GPU

scripts/bench.sh

Repeatable load test at several concurrency levels

scripts/bench-classroom.sh

Simulated class: concurrent users running OpenCode-like agent sessions

scripts/gen-api-keys.sh

Generates per-user API keys for nginx, locally

config/profiles/

Serving profiles (classroom-64k, classroom-64k-high-concurrency, deep-128k)

config/nginx.example.conf

HTTPS reverse proxy

config/opencode.example.json

OpenCode V2 provider configuration

.env.example

All server settings, no secrets

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