159 lines
8.2 KiB
Markdown
159 lines
8.2 KiB
Markdown
# llama.cpp (llama-server)
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In-cluster LLM inference via llama.cpp's `llama-server`, serving a local model
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on the NUCBox APU (AMD Ryzen AI Max 395 / Strix Halo, Radeon 8060S, ~120 GiB
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unified memory: ~90 GiB VRAM / 30 GiB CPU RAM).
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LiteLLM (`litellm/`) points at these in-cluster Services instead of the
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external `10.88.20.12:11434` Ollama endpoint.
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## Layout
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One Deployment + Service **per model**, all in namespace `llamacpp`, all pinned
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to the NUCBox (`nodeSelector: {kubernetes.io/arch: amd64, hardware: high-memory}`):
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| Alias | Model | GGUF | Service | Args ref |
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|--------------------------|--------------------------------|-------------------------------------------------------|------------------------------------------------------|----------|
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| `deepseek-v4-flash-0731` | DeepSeek-V4-Flash-0731 (MoE) | unsloth/DeepSeek-V4-Flash-0731-GGUF (UD-IQ1_M, ~87 GiB) | `llamacpp-deepseek-v4-flash-0731.llamacpp:80` | [args-deepseek-v4-flash-0731.md](args-deepseek-v4-flash-0731.md) |
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| `qwen3.5-4b` | Qwen3.5-4B (MTP) | unsloth/Qwen3.5-4B-MTP-GGUF (`UD-Q4_K_XL`) | `llamacpp-qwen35-4b.llamacpp:80` | — |
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DeepSeek-V4-Flash-0731 is a Mixture-of-Experts model (256 experts, 6 active per
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token) with MLA attention, so only a small fraction of the weights is computed
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per token. The full ~87 GiB of IQ1_M weights is loaded into the unified memory
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pool.
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> **Previously** the NUCBox ran two co-resident Qwen3.6 models (a 27B dense and
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> a 35B-A3B MoE "flash"). DeepSeek-V4-Flash-0731 (~87 GiB) nearly fills the
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> whole 90 GiB VRAM pool on its own, so both Qwen models were removed to make
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> room. Their GGUF files are deleted from the shared PVC by the new pod's
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> `fetch-model` initContainer on first boot. Their deployment arguments are
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> still documented for redeployment:
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> - [args-qwen36-27b.md](args-qwen36-27b.md) — dense 27B (deeper reasoning)
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> - [args-qwen36-35b-a3b.md](args-qwen36-35b-a3b.md) — MoE 35B-A3B "flash" (fast)
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Model files are downloaded idempotently by an initContainer into a shared
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hostPath PVC (`/data/llamacpp/models` on the NUCBox), so pods survive reboots
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without re-downloading. The UD-IQ1_M GGUF is split across 3 shards
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(`-00001-of-00003` … `-00003-of-00003`); llama.cpp auto-loads all shards when
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pointed at the first one.
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## GPU / Vulkan
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The `server-vulkan` image (`ghcr.io/ggml-org/llama.cpp:server-vulkan`) bundles
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the Mesa/RADV Vulkan driver, which supports the Radeon 8060S (RDNA 3.5). The
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Vulkan backend supports the `IQ1_M` matmul (including the MoE `matmul_id`
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variant), so the whole model runs on the GPU. `deepseek4` is a brand-new arch
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(2026-07), so the floating `server-vulkan` tag is used to pull a recent enough
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build — pin to a specific `server-vulkan-bXXXX` tag once a known-good one is
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verified.
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The container mounts `/dev/dri` and runs `privileged: true` — the simplest
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reliable way to give Vulkan access to the DRM render node on k3s without a
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device plugin. Tighten later with `supplementalGroups` (the host's `render`
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group GID) if desired.
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### Verify the GPU is actually used
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```bash
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kubectl logs -n llamacpp deploy/llamacpp-deepseek-v4-flash-0731 | grep -iE 'vulkan|gpu|offload|device'
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```
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If only a CPU device shows up, the container can't see the GPU — check that
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`/dev/dri/renderD128` exists on the NUCBox and that the `amdgpu` module is loaded.
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## VRAM budget (single model)
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The model (~87 GiB IQ1_M) is almost the size of the entire 90 GiB VRAM pool, so
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it **cannot be fully offloaded**: `-ngl 999` would try to put all 43 layers into
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VRAM and overflow once the KV cache + Vulkan compute buffers are added. Instead
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`-ngl 38` offloads 38 of 43 layers to the GPU and keeps 5 layers (~10 GiB) on
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CPU RAM, leaving ~5 GiB of VRAM headroom for the KV cache, compute buffers, and
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fragmentation.
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**KV cache is f16, not q8_0** — the Vulkan backend has no Flash Attention for
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the `deepseek4` arch, and quantized V cache requires Flash Attention (llama.cpp
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hard-errors: *"quantized V cache was requested, but this requires Flash
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Attention"*). `deepseek4`/MLA models also require K and V cache types to be
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*identical*, so K cannot be quantized either. f16 MLA KV at 64k is ~5.7 GiB
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(576 K + 512 V elements/token/layer × 43 layers × 65536 tokens × 2 bytes) —
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larger than q8_0 would be, which is why `-ngl` is 38 rather than 40.
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Approximate VRAM usage:
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| Component | VRAM |
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|---------------------------------|-------------|
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| Weights (38 GPU layers) | ~77 GiB |
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| KV cache (f16, 64k, 1 slot) | ~5.7 GiB |
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| Vulkan compute buffers | ~2 GiB |
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| **Total in VRAM** | **~85 GiB** |
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| **Headroom (of 90 GiB)** | **~5 GiB** |
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5 layers (~10 GiB) live in CPU RAM (counted against the pod's cgroup memory
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limit, not VRAM). VRAM is exclusive to this model; the other NUCBox pods only
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compete for the 30 GiB CPU RAM.
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Several `deepseek4`-specific fused ops (Lightning Indexer, HC pre/comb/post)
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are not yet implemented in the Vulkan backend and fall back to CPU (logged as
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warnings, not fatal). Inference still works; it will speed up once those ops
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land in a future `server-vulkan` build.
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## Tuning
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The key knobs (in `deployment-deepseek-v4-flash-0731.yaml`):
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- `-ngl 38` — offload 38 of 43 layers to GPU. The model (~87 GiB) is nearly the
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whole 90 GiB VRAM pool, so full offload would overflow once the f16 KV cache +
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compute buffers are added. 5 layers (~10 GiB) on CPU leaves ~5 GiB VRAM
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headroom. Raise toward 43 if VRAM allows; lower (e.g. 36) if the pod OOMs /
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Vulkan runs out of device memory.
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- `-c 65536` — total KV-cache context (64k, the required minimum). 1 slot gets
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the full 64k. f16 MLA KV at 64k is ~5.7 GiB; capped at the minimum to maximise
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VRAM headroom. Raise if headroom allows.
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- `-np 1` — 1 parallel slot (the full 64k goes to a single concurrent request).
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Extra slots multiply the f16 KV cost (~5.7 GiB/slot); 1 slot keeps headroom
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maximal.
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- `--cache-type-k f16 --cache-type-v f16` — **f16 KV cache (NOT quantized).**
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The Vulkan backend has no Flash Attention for `deepseek4`, and quantized V
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cache requires Flash Attention. `deepseek4`/MLA models also require K and V
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cache types to be identical, so K cannot be quantized either. This is the
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reason `-ngl` is 38 rather than 40.
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- `--temp 1.0 --top-p 0.95` — default sampling parameters (DeepSeek-V4
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recommendation). These are server defaults; clients can override per request
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via the OpenAI-compatible API.
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- `--threads 8` — CPU threads for sampling + the 5 CPU-resident layers.
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## Memory accounting
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k8s sees only the ~30 GiB system RAM as allocatable (the ~90 GiB VRAM is
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reserved by firmware and managed by `amdgpu`). The GPU-resident model weights
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and KV cache live in VRAM and are **not** counted against the container's cgroup
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memory limit — that limit only covers CPU-side overhead, the mmap'd GGUF pages
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for the 5 CPU-resident layers (~10 GiB, resident during inference), and
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reclaimable page cache during load. If the pod is OOM-killed during model load
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or inference, raise the memory limit (and/or lower `-ngl` to push more layers
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to VRAM).
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## Adding / replacing a model
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1. Copy `deployment-deepseek-v4-flash-0731.yaml` → `deployment-<new>.yaml`;
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change the `model:` label, GGUF URL/file(s), `--alias`, and Service name.
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For split GGUFs, point `-m` at the first shard and download all shards in
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the `fetch-model` initContainer.
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2. Point LiteLLM at it in `litellm/litellm.yaml`:
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```yaml
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- model_name: <alias>
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litellm_params:
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model: openai/<alias>
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api_base: http://<service>.llamacpp/v1
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api_key: "sk-no-auth"
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```
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3. (No gen-apps.sh change needed — the `llamacpp` app already syncs the whole
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directory recursively.)
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4. Check the VRAM budget table above — at ~87 GiB this model nearly fills the
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90 GiB pool on its own, so co-locating another large model is not possible.
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The `qwen3.5-4b` alias is exposed through LiteLLM as a low-latency option for
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Home Assistant voice Assist. Select this model in the Home Assistant
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conversation/voice Assist provider configuration; the LiteLLM endpoint is
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`http://litellm-service.litellm:80/v1` from inside the cluster.
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