llmgw/docs/install.md

# Installing llmgw from nothing

Written for whoever is standing up the cluster: you have servers, you have root
on them, and nothing is installed yet. By the end there is a control plane, a
node in the fleet, and a model answering requests.

It assumes Debian or Ubuntu and adjusts nothing for other distributions beyond
the package names. Everything here has been done on a real cluster except where
this page says otherwise, and where it says otherwise it says so plainly.

## What llmgw does not install

Read this first, because it is the difference between an afternoon and a week.

llmgw runs engines and routes requests. It does **not** install GPU drivers,
CUDA, inference engines, or model weights. A node missing any of them still
registers, still reports `ready`, and still fails every replica placed on it —
so the useful order is to make a node able to serve *before* onboarding it, not
after.

| You provide | llmgw provides |
| --- | --- |
| NVIDIA driver and `nvidia-smi` | GPU discovery, classes, health |
| InfiniBand stack (`rdma-core`/OFED) | Fabric discovery and the invariant that a replica never spans one |
| vLLM, SGLang or llama.cpp | Launching them, the right flags, supervision |
| Model weights on disk | Placement, routing, accounting |

The reason is boring and unavoidable: drivers and engines are platform decisions
with kernel modules and version matrices attached, and distributing weights is
its own problem — hundreds of gigabytes that want a shared filesystem, not a
copy from somebody's laptop. It is a known gap, not an oversight.

## 0. Before you begin

Four machines is a reasonable first cluster: one control plane and three nodes.
One of each also works.

**The control plane** needs no GPU. It is a single process holding a SQLite
file; 2 GB of RAM and a few gigabytes of disk is generous. It must be reachable
from every node on the agent port, and from your users on the client port.

**Nodes** need whatever the model needs, plus about 8 GB for the operating
system and the agent. They dial the control plane; the control plane never dials
them, so nodes need no inbound access except your own SSH.

**Ports**

| Port | Who connects | Expose to |
| --- | --- | --- |
| 8080 | clients, the web UI | your users, usually via a reverse proxy |
| 7443 | node agents | the node network only |
| 9090 | `llmgw ctl`, metrics, pprof | loopback only — it serves pprof, which can dump the process heap, and API keys live in that heap |

Decide now which address nodes will use to reach the control plane. It goes into
a certificate and changing it later means reissuing one.

## 1. The binary

One static binary, no runtime dependencies, the same on every machine.

```
VERSION=v0.6.0    # check the releases page for the current tag
curl -fsSLO https://gitea.mynodes.xyz/ilker/llmgw/releases/download/$VERSION/llmgw-$VERSION-linux-amd64
curl -fsSL  https://gitea.mynodes.xyz/ilker/llmgw/releases/download/$VERSION/checksums.txt | sha256sum -c --ignore-missing
sudo install -m0755 llmgw-$VERSION-linux-amd64 /usr/local/bin/llmgw
llmgw --version
```

`arm64` and `darwin` builds are published alongside. They are built with
`CGO_ENABLED=0` deliberately, so a binary built today runs on a node whose libc
is older than the builder's.

To build instead of download, you need Go 1.26 or later and nothing else:

```
git clone https://gitea.mynodes.xyz/ilker/llmgw && cd llmgw
CGO_ENABLED=0 go build -o llmgw ./cmd/llmgw
```

## 2. The certificate authority

Agents authenticate to the control plane with mutual TLS, and a node's identity
is taken from its certificate's common name. A public authority cannot supply
these: Let's Encrypt and its peers stopped issuing certificates carrying the
`clientAuth` usage, so nothing they hand out can authenticate an agent.

Run this wherever you intend to keep the authority. It talks to nothing and
works on files, so it does not have to be a cluster machine — and there is an
argument that it should not be.

```
sudo llmgw ctl cert init-ca --dir /etc/llmgw/pki
```

That writes `ca.pem` and `ca-key.pem`. **Whoever holds `ca-key.pem` can mint any
node's identity**, so it stays here; `ca.pem` is copied to every machine and is
not a secret.

Now the control plane's own certificate. Every address an agent might dial must
appear in `--host`, bare IP addresses included — hostname verification reads
subject alternative names and ignores the common name entirely, so a certificate
naming the host only in its subject is rejected by every client:

```
sudo llmgw ctl cert issue-server llmgw-ctl \
  --host 10.0.0.140 --host llmgw-ctl.internal \
  --dir /etc/llmgw/pki --out /etc/llmgw
```

This covers the fleet-facing port only. The client-facing API is public and
belongs behind an ordinary publicly trusted certificate, usually terminated by a
reverse proxy in front of port 8080.

## 3. The control plane

### The user and directories

```
sudo useradd --system --home /var/lib/llmgw --shell /usr/sbin/nologin llmgw
sudo install -d -o llmgw -g llmgw -m 0750 /var/lib/llmgw
sudo install -d -m 0755 /etc/llmgw
sudo chown root:llmgw /etc/llmgw/ca.pem /etc/llmgw/server.pem /etc/llmgw/server-key.pem
sudo chmod 0640 /etc/llmgw/server-key.pem
```

### The configuration

`/etc/llmgw/server.yaml`. This is the smallest file that is not a toy: mutual
TLS on, authentication on, one model, and limits that apply to anyone nobody has
given limits to.

```yaml
listen: ":8080"
admin_listen: "127.0.0.1:9090"
agent_listen: ":7443"

agent_tls:
  ca: /etc/llmgw/ca.pem
  cert: /etc/llmgw/server.pem
  key: /etc/llmgw/server-key.pem

auth:
  enabled: true
  store: /var/lib/llmgw/llmgw.db
  signup_open: true
  # Applied to any key no scope gives a limit to. Without these, a project
  # created without limits is metered by nothing at all, and the first sign of
  # that is the bill or the outage.
  default_rpm: 60
  default_tpm: 200000

landing:
  contact: "platform@example.com"

# Start with none. Models are added while the cluster runs — see step 7 —
# and anything listed here is declarative: it is in the catalog on every start
# and cannot be removed without editing this file.
models: []
```

`admin_listen` is on loopback on purpose. It serves pprof, which can dump the
process heap, and API keys sit in that heap in plaintext. To see the dashboard
from elsewhere, promote an account and use `/admin` on the client port — do not
move this listener.

The store file is created on first start; there is no separate migration step.

### The service

```
sudo cp deploy/systemd/llmgw-server.service /etc/systemd/system/
sudo systemctl daemon-reload
sudo systemctl enable --now llmgw-server
systemctl status llmgw-server
```

### Your account

Accounts are created and approved from a shell on this machine, deliberately:
having a shell here is what makes someone an administrator, and there are no
roles to get subtly wrong.

```
sudo -u llmgw llmgw users create you@example.com --password 'change me now'
# prints a user id, usr-xxxxxxxx
sudo -u llmgw llmgw users promote usr-xxxxxxxx
```

`promote` is what grants `/admin`. Sign in at `http://<control-plane>:8080/login`,
change the password, and issue yourself a key from the profile page.

### Check it

```
curl -s -o /dev/null -w '%{http_code}\n' http://127.0.0.1:8080/health   # 200, with an empty body
sudo -u llmgw llmgw ctl status
```

`ctl status` will report no nodes and no models, which is correct — nothing has
been added yet. It is worth running now so its output is familiar before you
need it.

## 4. Preparing a node

**Do this before onboarding.** A node missing its driver or engine registers
healthy and fails on first placement, and the reason is three logs away.

### The driver

```
sudo apt-get update
sudo apt-get install -y nvidia-driver-550   # or whatever your cards need
sudo reboot
nvidia-smi                                   # must print your GPUs
```

`nvidia-smi` must be on `PATH` for the user the agent runs as, which is root.
Containerised driver installs often put it elsewhere; if so, note the path and
set `discovery.nvidia_smi` in the agent configuration rather than moving it.

A node with no GPU at all is fine and is not an error. It reports zero cards and
can host CPU profiles, which is a good way to try llmgw before committing
hardware to it.

### NVLink, if your GPUs have it

The published binary cannot see NVLink topology. NVML is the only source of it,
its bindings need cgo, and the portable binary is built without cgo on purpose
so it runs on a node whose libc is older than the builder's.

Without that topology, a tensor-parallel group takes the lowest free GPU
indices. On an SXM board with NVSwitch that is correct, because every card is
equidistant. On a PCIe box where NVLink bridges pair specific cards, a group
straddling two bridges falls back to PCIe and runs several times slower — and
nothing reports it, because from the outside it is simply a replica that is
slow.

For those nodes, install the `-nvml` build published beside the portable one:

```
sudo install -m0755 llmgw-$VERSION-linux-amd64-nvml /usr/local/bin/llmgw
```

It is dynamically linked, so it needs a libc no older than the builder's and the
NVIDIA driver present. Check it took effect with:

```
llmgw ctl nodes --json | grep -c nvlink_peers    # 0 means the topology is unknown
```

`llmgw_gpu_nvlink_peers` in the metrics says the same thing across a fleet: a
flat zero everywhere means the topology is unknown rather than absent.

### InfiniBand, if you have it

```
sudo apt-get install -y rdma-core ibverbs-utils infiniband-diags
ibstat            # must list your HCAs with State: Active
ls /sys/class/infiniband
```

The agent checks what you declare against what the node has. An adapter that is
not there, or that has no ACTIVE port — physically linked but with no subnet
manager, which survives a reboot and looks like a software problem — takes that
fabric out of service on this node, loudly, in the log. Without that check the
replica starts, passes its health checks, and hangs on its first all-reduce.

Fabric membership is configuration, not discovery, and there is no way around
that: OpenSM's default subnet prefix is the same on every fabric, so a matching
prefix proves nothing and a wrong guess produces a replica that starts, passes
health checks, and then hangs on its first all-reduce. You name the fabric when
you onboard the node, and two nodes are only in the same fabric if you said so.

### An engine

Pick what you will actually serve. For a first cluster, llama.cpp is the least
trouble and runs without a GPU.

```
# llama.cpp, from a release build
sudo apt-get install -y libgomp1          # it will not start without this
sudo mkdir -p /opt/llama.cpp
# unpack a llama.cpp release into /opt/llama.cpp
/opt/llama.cpp/llama-server --version
```

That `libgomp1` line is not padding. A `llama-server` binary missing it exists,
is executable, and cannot start — and the node reports `ready` throughout.

For vLLM, a virtualenv on the node:

```
sudo apt-get install -y python3-venv
sudo python3 -m venv /opt/venvs/vllm
sudo /opt/venvs/vllm/bin/pip install vllm
/opt/venvs/vllm/bin/vllm --version
```

Check the engine still takes the flags llmgw builds for it, which drift between
releases:

```
llmgw ctl engine-check vllm --bin /opt/venvs/vllm/bin/vllm
```

### Weights

Put them where the node can read them. There is no distribution mechanism yet;
`scp`, a shared filesystem, or the engine's own downloader are all valid, and
which you want depends on how many nodes you have.

```
sudo install -d -o root -g root /var/lib/llmgw-models
# then place your .gguf or model directory there
```

For a first model, something small: a 3B at `q4_k_m` is about 2 GB, runs on
CPU, and answers fast enough to prove the whole path works.

### Letting llmgw fetch them

The simple arrangement, and the default: every node keeps its own copy. Point a
model's `source` at a file to download and the agent fetches it once, before the
engine is launched:

```yaml
source: hf:Qwen/Qwen2.5-Coder-3B-Instruct-GGUF/qwen2.5-coder-3b-instruct-q4_k_m.gguf
# or an ordinary URL
source: https://models.internal/qwen2.5-coder-3b-q4_k_m.gguf
```

Eight shards of one replica starting together produce one download, not eight.
An interrupted fetch resumes rather than starting over, and a partial file is
never visible under the model's name — so a half-downloaded model cannot be
picked up by the next scan and handed to an engine.

Three kinds of source, and only one of them is fetched:

| `source` | What happens |
| --- | --- |
| `/var/lib/llmgw-models/x.gguf` | Checked, never copied. However it got there. |
| `hf:org/repo/file.gguf`, `https://…` | Fetched once into the cache, then reused |
| `Qwen/Qwen2.5-7B-Instruct` | Left alone — vLLM and SGLang download repositories themselves |

Which means the exotic arrangements need no configuration at all. Weights on a
Lustre mount, or an image exported over the fabric with SRP or NVMe-oF, are
simply *present*, and present means nothing is fetched.

### Telling llmgw where they are

Optional, and unset by default. Naming the directories in the agent's
`model_dirs` lets a node report which models it can see, and placement then
prefers a node that already holds one over a node that must first move tens of
gigabytes:

```yaml
model_dirs:
  - /var/lib/llmgw-models
  - /mnt/lustre/models
```

llmgw does not manage that storage and does not care how the files arrived. A
Lustre or NFS mount, an image exported over the fabric with SRP or NVMe-oF,
local NVMe, or an `scp` all look identical to it. A node with nothing configured
reports nothing and is treated exactly as every node was before this existed.

**It matters most when your fabrics are not connected to each other.** Weights
in `ib-a` are not weights in `ib-b`: the only path between them is the
management network, which is the slow one. `llmgw ctl status` shows what each
fabric holds, so a model seeded into one fabric does not quietly leave the rest
of the cluster unable to serve it.

How much disk a node needs follows from the models rather than a rule of thumb.
`llmgw ctl model plan <repo>` prints the sizes; as a starting point, a 70B at
bf16 is 141 GB and the same model at q4 is 43 GB.

## 5. Adding the node

Two halves, because they need different access: building the machine needs the
hypervisor, and issuing its identity needs the certificate authority. Where one
person holds both — the common case — one command does it:

```
llmgw-add-node \
  --name llmgw-node01 --ip 10.0.0.141/24 \
  --server https://10.0.0.140:7443 \
  --cores 16 --memory 131072 --disk 200 \
  --fabric ib-a:mlx5_0,mlx5_1 \
  --llamacpp-bin /opt/llama.cpp/llama-server
```

**The VM name, the hostname, the node id and the certificate's common name are
all the same string.** They do not have to be, and every time they are not,
somebody spends an afternoon working out which of the four a message refers to.

For a machine that already exists — bare metal, or a VM you built yourself — skip
provisioning and issue the identity only:

```
llmgw-onboard-node \
  --node llmgw-node01 --addr 10.0.0.141 \
  --server https://10.0.0.140:7443 \
  --fabric ib-a:mlx5_0,mlx5_1 \
  --llamacpp-bin /opt/llama.cpp/llama-server
```

Onboarding needs SSH from the CA host to the node. It waits for SSH rather than
failing on it, and learns the host key of a machine it has not seen — a new
machine spends its first minute regenerating host keys and restarting sshd.

It also checks that a configured engine actually runs, and says so loudly when
it does not. That check exists because the failure is otherwise invisible.

Within a few seconds:

```
llmgw ctl nodes
```

```
NODE          STATE  GPUS      MEM   CLASSES   FABRICS  REPLICAS  LAST SEEN
llmgw-node01  ready  8/8 free  503G  h100-80g  ib-a     0         2s
```

If it does not appear, the control plane's log says why. A certificate whose
common name does not match the claimed node id is refused by design — that check
is what makes a node's identity something it proves rather than something it
picks.

Repeat for each node. Nodes in the same fabric must be given the same `--fabric`
name; nodes with no InfiniBand take no `--fabric` at all and are each their own
island, which is fine for single-node models.

## 6. Setting the node's CPU model, if it is a VM

Proxmox defaults a VM to `kvm64`, which hides AVX, AVX2, FMA and even SSE4.2
from the guest. Inference then falls back to scalar code and is not slightly
slower but unusable. Measured on one 8-core VM, same weights, same threads, the
only change being `--cpu host`:

| Qwen2.5-Coder | kvm64 | host |
| --- | ---: | ---: |
| 1.5B q4_K_M | 5.80 | **39.09** tok/s |
| 3B q4_K_M | 2.90 | **20.51** |
| 7B q4_K_M | 1.33 | **9.88** |

`llmgw-provision-node` sets `--cpu host` for you. A VM you built yourself
probably did not, and nothing about it looks broken — every number is simply
small. Check with `grep -qw avx2 /proc/cpuinfo` on the node.

## 7. Adding a model

Write the definition. This is the same shape a model takes in `server.yaml`, so
an entry moves between the two by being copied:

```yaml
# qwen-coder-3b.yaml
name: qwen2.5-coder-3b-instruct
aliases: [coder]
source: /var/lib/llmgw-models/qwen2.5-coder-3b-instruct-q4_k_m.gguf
routing_profile: chat
# What a client may use in one request. Worth stating: llama.cpp divides
# --ctx-size across its parallel slots, so a pool of 65536 over two slots is
# 32768 each, and nothing else will tell you that.
context_length: 32768
max_output: 4096
scaling: { min: 1, max: 1 }
profiles:
  - name: cpu
    engine: llamacpp
    weight: 1
    priority: 100
    startup_timeout: 5m
    min_memory_gb: 8
    args: ["--ctx-size", "65536"]
```

```
llmgw ctl model add -f qwen-coder-3b.yaml
llmgw ctl models
```

```
MODEL                      SERVING  REPLICAS  STATUS   ALIASES
qwen2.5-coder-3b-instruct  yes      1/1       ready    coder
```

No restart, and it survives one: models added this way are stored, while
anything in `server.yaml` is declarative and returns on every start.

### Models that load when someone asks

A cluster with more models than GPUs can hold every model in its catalog and
only the wanted ones in its cards:

```yaml
scaling: { min: 0, max: 1, idle_timeout: 15m }
```

`min: 0` with a `max` above it means load on demand. The first request for the
model starts it and waits briefly; if it is not serving by then the client gets
a 503 with `Retry-After` and an honest "the model is loading", rather than being
told nothing serves it — a client told to retry succeeds where one told the
model does not exist gives up. Once nobody has asked for `idle_timeout`, the
replicas are stopped and the cards go back to the pool.

`min: 0, max: 0` is different and equally useful: configured, and deliberately
not running. A model with no `scaling` stanza at all gets that, because making
every unscaled model loadable on demand would let any client request start a
70B nobody intended to run.

Two things to weigh. A cold start is visible to whoever triggers it, so this
suits the long tail people are experimenting with rather than the two or three
models used daily — keep those at `min: 1`. And stopping a replica throws away
the KV cache prefix routing spent its effort filling, so an idle timeout wants
to be minutes rather than seconds.

When the cluster is full, a model somebody asks for will stop an idle one to
make room. Only idle replicas of other on-demand models: a model at `min: 1` is
a promise that it stays up, and breaking that to serve a different request would
make every guarantee in the catalog conditional on load. Only single-node
replicas, because evicting a multi-node one frees several machines and costs
tens of minutes to rebuild — a decision worth making deliberately. One at a
time. And never a replica that has just started, which is what stops two models
on a cluster with room for one from taking turns evicting each other.

For a GPU model, name the class and the width instead:

```yaml
profiles:
  - name: tp2
    engine: vllm
    gpu_class: h100-80g    # as `llmgw ctl nodes` reports it
    tp: 2
    priority: 100
```

If it does not place, ask:

```
llmgw ctl explain-placement qwen2.5-coder-3b-instruct
```

which names every candidate and why each was rejected, rather than leaving you
to infer it.

## 8. The first request

```
curl http://<control-plane>:8080/v1/chat/completions \
  -H "Authorization: Bearer $LLMGW_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "coder", "messages": [{"role":"user","content":"hello"}]}'
```

Or open `/chat` in a browser and use it without a key at all — the page carries
your session, and the request still goes through admission, rate limits, budget
and accounting exactly as an API call does.

## Watching it

The control plane exports both halves on one endpoint: what the cluster is
serving — requests, latency, time to first token, tokens, admission, per
endpoint — and what it is made of, per node and per GPU, including memory,
temperature, ECC faults and utilisation.

**The dashboard is the answer for most clusters.** `/admin` shows capacity per
GPU class, free whole nodes per fabric, which replicas are holding hardware
nothing is asking for, and the same node and model tables `ctl` prints — with
no monitoring stack to stand up beside it. One binary, no dependencies, which
is the point.

Prometheus is worth adding when you want history, alerts, or one view across
several clusters. The dashboard shows what is true now; it keeps nothing.

Metrics then get their own listener, because the admin one also carries pprof
and pprof can dump the process heap, where API keys sit in plaintext:

```yaml
admin_listen:   "127.0.0.1:9090"   # dashboard, management API, pprof — keep local
metrics_listen: "0.0.0.0:9091"     # metrics only, safe to reach
```

One scrape target for the whole fleet. The node and GPU figures come from the
control plane, which already holds them, so there is nothing to install on the
nodes and no Prometheus configuration per machine.

A starting configuration, with the alerts worth having, is in
[deploy/monitoring/prometheus.yml](../deploy/monitoring/prometheus.yml).

Two figures repay knowing about. `llmgw_gpu_utilization_percent` is absent
rather than zero when the driver does not report it — a flat zero across a
fleet means the measurement is missing, not that the fleet is idle. And
`llmgw_gpu_nvlink_peers` at zero everywhere means the topology is unknown,
which is what a binary built without the nvml tag reports.

## When something does not work

**The node is `ready` but every replica fails.** Almost always a missing engine
or weights. `llmgw ctl replicas` shows the shard's error and the engine's last
output, which is the actual diagnosis.

**A model will not place.** `llmgw ctl explain-placement <model>` names each
node and the reason: wrong GPU class, not enough free cards, too little memory,
no fabric, a label it does not carry.

**Everything is slow.** On a VM, check AVX2 (step 6). On CPU, check the
quantisation — the difference between `q8_0` and `q4_k_m` is roughly a factor of
two. `llmgw ctl status` shows how full the cluster is.

**Tool calls come back as text.** The model made the call and the engine did not
parse it. `llmgw ctl tool-check --endpoint http://<node>:<port>` distinguishes
that from a model ignoring its tools, which has a different fix.

**Requests fail at exactly sixty seconds.** `timeouts.response_header` is a
liveness check for streaming and a total generation budget for non-streaming
requests. On a slow model, set `timeouts.generation` higher.

## What to read next

- `docs/node-onboarding.md` — adding nodes in more detail, and what onboarding
  deliberately does not do
- `docs/onboarding.md` — giving people accounts and keys
- `llmgw-plan.md` — why the system is shaped the way it is

## Known gaps, so you find them here rather than at 2am

- **Weights are yours to distribute.** No cache, no fetch, no progress.
- **Engines are yours to install.** Onboarding verifies one runs; it installs
  nothing.
- **One control plane.** No HA yet; it is a single process with a SQLite file.
  Back up `/var/lib/llmgw/llmgw.db`.
- **Multi-node replicas have not been run on real InfiniBand.** The scheduling,
  the fabric invariant and the rendezvous are exercised against simulated
  hardware only. Single-node placement on real GPUs is the well-trodden path;
  if you are the first to run a TP group across real cards, expect to find
  something.