LLM-Server/docs/architecture.md

# Architecture

> **Source of truth:** [`docker-compose.yml`](../docker-compose.yml) → each service below. This page explains *why*; flags, values and defaults live in the source. If they disagree, the source wins.

Terms as defined in [`CONTEXT.md`](../CONTEXT.md).

Every workload calls the **gateway** (`omniroute`) with its own API key. The gateway forwards each call over a **provider connection** to the one **backend** on the GPU: `llama-server` (main model). `qdrant` sits beside them on the same Docker network. It isn't a model, so it isn't behind the gateway.

## GPU access

`llama-server` carries these GPU settings in compose:

- **`group_add` uses numeric GIDs, not group names.** Docker resolves a *named* `group_add` entry against the container's own `/etc/group`, not the host's. That fails unpredictably when the image doesn't define the group, and it gets worse when several GPU services start at once. `scripts/update.sh` resolves the host's real GIDs into `HOST_VIDEO_GID` / `HOST_RENDER_GID` (see [operations → Host-resolved values](operations.md#host-resolved-values)).
- **`GPU_MAX_HW_QUEUES`** works around [ROCm/ROCm#5706](https://github.com/ROCm/ROCm/issues/5706): the card sits pinned at full load and boost clock whenever two HIP contexts touch it. Keep it set on every GPU service.

Background: [`research/rocm-gpu-pin-and-render-group.md`](research/rocm-gpu-pin-and-render-group.md). Its service names are older than the current ones, but the findings still apply.

## llama-server

The main model's backend. It is always running. Lazytainer used to idle-stop it, but was removed because its idle detection could never fire behind the gateway ([`research/lazytainer-omniroute-idle-stop.md`](research/lazytainer-omniroute-idle-stop.md)).

- **Model file** (`LLAMA_MODEL_FILE`): the quant choice and its VRAM budget are in [`research/qwen3.8-27b-quant.md`](research/qwen3.8-27b-quant.md).
- **`LLAMA_GPU_LAYERS`**: the default puts every layer on the GPU. The model is dense, not Mixture-of-Experts, so this is the only RAM-offload knob. `--n-cpu-moe`, `--cpu-moe` and `--override-tensor "exps"` do nothing here. There is no SSD tier to enable either. llama.cpp mmaps the model file, so if GPU and RAM ever can't hold it, the OS pages the rest in from disk as a slow, implicit last resort. Explicit tiered offload is an [open llama.cpp request](https://github.com/ggml-org/llama.cpp/discussions/12507).
- **`LLAMA_CTX_SIZE`**: set to the model's own maximum (`max_position_embeddings`). It only fits beside the weights because the KV cache is quantized (`--cache-type-k/v`). An fp16 KV cache at that size would not fit the card. The numbers are in the quant research doc.
- **`LLAMA_PARALLEL`** is the backend's number of **slots**. It's kept low on purpose: more slots meant more contended prefill, which pushed requests past the gateway's timeouts ([gateway → Timeouts](gateway.md#timeouts)). Requests beyond the slot count wait in llama.cpp's own queue, which has no timeout. So the timeout that matters is the gateway's per-connection one.
- **Per-slot context = `LLAMA_CTX_SIZE / LLAMA_PARALLEL`.** llama.cpp divides the context across slots, and each request gets one slot's share. Every coding CLI's context-window setting must use this number, not the raw context size. Real sessions already fill a large share of it, so check that the per-slot figure stays comfortably above observed usage before lowering either value.
- **`--cache-reuse`** reuses cached KV for any matching prompt chunk instead of re-prefilling from scratch. That fixes the root cause behind the gateway's non-ping SSE timeout ([`research/omniroute-non-ping-sse-stream-timeout.md`](research/omniroute-non-ping-sse-stream-timeout.md)). It pairs with OmniRoute's prompt-cache affinity (a dashboard setting, on by default), which keeps a conversation on the same slot so that there is a matching prefix to reuse.
- **`--jinja`** enables the chat template that tool calling depends on. Without it, tool-use requests fail outright.
- **Tool-calling reliability** of this model family on llama.cpp has open upstream parser bugs: [`research/qwen3.8-27b-tool-calling.md`](research/qwen3.8-27b-tool-calling.md).
- **Speculative decoding (DFlash2):** `--spec-type draft-dflash` with a separate drafter GGUF (`LLAMA_DRAFT_MODEL_FILE`), fetched by `downloader-draft`. It replaced MTP (draft heads inside the target GGUF). `LLAMA_SPEC_N_MAX` is the draft length. Upstream: [llama.cpp#27342](https://github.com/ggml-org/llama.cpp/pull/27342). Draft acceptance can collapse at high slot counts ([llama.cpp#27117](https://github.com/ggml-org/llama.cpp/issues/27117)), another reason `LLAMA_PARALLEL` stays low.
- **No classifier backend:** the separate `qwen-classifier` was removed to free VRAM for the drafter. Anything that needs a small "fast" model must use the main model.
- **No host port**: see [network access → Backends](network-access.md#backends).

## omniroute

The gateway. Its runtime configuration (provider connections, API keys) lives in its own database and is managed through the dashboard, not in this repo: see [`gateway.md`](gateway.md).

- **`OMNIROUTE_MEMORY_MB` + `mem_limit`**: the default heap is sized for dashboard-only use. OmniRoute's Docker guide recommends more for coding-agent traffic, and `mem_limit` is the matching container ceiling.
- **`stop_grace_period`**: SQLite needs time on shutdown to checkpoint its WAL back into the main database file. If this is shortened, recent writes (keys, provider connections) can be lost.
- **Healthcheck is a TCP connect**, not an HTTP probe. The image has only `node` (no curl, wget or python), and OmniRoute's Docker guide accepts a TCP probe as a liveness check.

## qdrant

A vector store. It has no authentication, so its port is bound to the server's loopback interface only: see [network access → qdrant](network-access.md#qdrant).