# llama.cpp (llama-server)

In-cluster LLM inference via llama.cpp's `llama-server`, serving local models on
the NUCBox APU (AMD Ryzen AI Max 395 / Strix Halo, Radeon 8060S, 128 GiB unified
memory: 32 GiB RAM / 96 GiB VRAM).

This replaces the bare-metal Ollama setup for models that benefit from
always-loaded weights + tuned batching. LiteLLM (`litellm/`) points at these
in-cluster Services instead of the external `10.88.20.12:11434` Ollama endpoint.

## Layout

One Deployment + Service **per model**, all in namespace `llamacpp`, all pinned
to the NUCBox (`nodeSelector: {kubernetes.io/arch: amd64, hardware: high-memory}`):

| Alias             | Model                      | GGUF                                                  | Service                                   |
|-------------------|----------------------------|-------------------------------------------------------|-------------------------------------------|
| `qwen3.6-27b`     | Qwen3.6-27B (dense)        | unsloth/Qwen3.6-27B-MTP-GGUF (Q4_K_XL, ~16 GiB)      | `llamacpp-qwen36-27b.llamacpp:80`         |
| `qwen3.6-35b-a3b` | Qwen3.6-35B-A3B (MoE/flash)| unsloth/Qwen3.6-35B-A3B-MTP-GGUF (Q4_K_XL, ~20 GiB)  | `llamacpp-qwen36-35b-a3b.llamacpp:80`     |

The 35B-A3B is a Mixture-of-Experts model (3B active params per token), so
inference is significantly faster than the dense 27B despite more total weights —
hence the "flash" label. Use it for latency-sensitive workloads; use the 27B for
deeper reasoning.

Model files are downloaded idempotently by an initContainer into a shared
hostPath PVC (`/data/llamacpp/models` on the NUCBox), so pods survive reboots
without re-downloading.

## GPU / Vulkan

The `server-vulkan` image (`ghcr.io/ggml-org/llama.cpp:server-vulkan`) bundles
the Mesa/RADV Vulkan driver, which supports the Radeon 8060S (RDNA 3.5). Full
layer offload (`-ngl 999`) puts model weights entirely in the 96 GiB VRAM pool.

The container mounts `/dev/dri` and runs `privileged: true` — the simplest
reliable way to give Vulkan access to the DRM render node on k3s without a
device plugin. Tighten later with `supplementalGroups` (the host's `render`
group GID) if desired.

### Verify the GPU is actually used

```bash
kubectl logs -n llamacpp deploy/llamacpp-qwen36-27b     | grep -iE 'vulkan|gpu|offload|device'
kubectl logs -n llamacpp deploy/llamacpp-qwen36-35b-a3b  | grep -iE 'vulkan|gpu|offload|device'
```

If only a CPU device shows up, the container can't see the GPU — check that
`/dev/dri/renderD128` exists on the NUCBox and that the `amdgpu` module is loaded.

## VRAM budget (both models co-resident)

Both models run simultaneously on the same 96 GiB VRAM pool. Approximate usage:

| Model             | Weights  | KV cache                                  | Subtotal |
|-------------------|----------|-------------------------------------------|----------|
| qwen3.6-27b       | ~16 GiB  | ~16 GiB  (q8_0, 131k total, 1 slot)      | ~32 GiB  |
| qwen3.6-35b-a3b   | ~20 GiB  | ~9 GiB   (q8_0, 262k total, 131k/slot)   | ~29 GiB  |
| **Total**         |          |                                           | **~61 GiB** |

~35 GiB headroom — comfortable. Both models' KV caches are quantized
to q8_0 (halved vs f16, ~negligible quality loss), which makes the dense 27B's
large-context KV affordable (~256 KiB/token f16 → ~128 KiB/token q8_0). The
a3b's KV is tiny (~72 KiB/token) so its large context is nearly free.

## Tuning

The key knobs (in each `deployment-*.yaml`):

- `-ngl 999` — offload all layers to GPU. Reduce only if VRAM is tight.
- `-c` — total KV-cache context. The 27B runs `-c 131072 -np 1` (single slot
  gets the full 131k); the a3b runs `-c 262144 -np 2` (2 slots × 131k each).
  The MoE a3b's KV cache is ~72 KiB/token so large context is cheap; the dense
  27B's is ~256 KiB/token (f16) / ~128 KiB/token (q8_0), which is why the 27B
  uses q8_0 KV to keep 131k affordable (~16 GiB) and sticks to 1 slot.
- `-np` — parallel slots (concurrent requests). Each extra slot multiplies
  KV-cache VRAM usage. The 27B uses 1 slot (full 131k to the single request,
  dense KV is the constraint); the a3b uses 2 slots (131k each, cheap MoE KV).
  Bump higher only if you need more concurrent throughput.
- `--cache-type-k q8_0 --cache-type-v q8_0` (both) — quantize the KV cache to
  q8_0, halving KV VRAM with ~negligible quality loss. Essential for the dense
  27B at 131k (f16 would be ~32 GiB KV alone); nearly free headroom on the a3b.
  Drop to q4_0 for even less VRAM if retrieval quality allows.
- `--threads 8` — CPU threads for sampling/overhead. Mostly irrelevant under
  full GPU offload; tune if CPU-bound.

## Memory accounting

k8s sees only the ~32 GiB system RAM as allocatable (the 96 GiB VRAM is
reserved by firmware and managed by `amdgpu`). The model weights and KV cache
live in VRAM and are **not** counted against the container's cgroup memory
limit — that limit only covers CPU-side overhead and the mmap'd GGUF pages
during load. If the pod is OOM-killed during model load, raise the memory limit.

## Adding a model

1. Copy `deployment-qwen36-27b.yaml` → `deployment-<new>.yaml`; change the
   `model:` label, GGUF URL/file, `--alias`, and Service name.
2. Point LiteLLM at it in `litellm/litellm.yaml`:
   ```yaml
   - model_name: <alias>
     litellm_params:
       model: openai/<alias>
       api_base: http://<service>.llamacpp/v1
       api_key: "sk-no-auth"
   ```
3. (No gen-apps.sh change needed — the `llamacpp` app already syncs the whole
   directory recursively.)
4. Check the VRAM budget table above — two large models may not coexist.