110 lines
5.6 KiB
Markdown
110 lines
5.6 KiB
Markdown
# Deployment arguments — `deepseek-v4-flash-0731`
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Reference for the llama-server flags used in
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`deployment-deepseek-v4-flash-0731.yaml`. Keep this in sync if the
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Deployment is edited.
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## Model & source
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- **Model:** `deepseek-ai/DeepSeek-V4-Flash-0731` (DeepSeek-V4-Flash, 0731 weights)
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- **Quantization:** `unsloth/DeepSeek-V4-Flash-0731-GGUF` → `UD-IQ1_M`
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(Unsloth Dynamic IQ1_M), **split across 3 shards** (~87 GiB total):
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- `DeepSeek-V4-Flash-0731-UD-IQ1_M-00001-of-00003.gguf`
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- `DeepSeek-V4-Flash-0731-UD-IQ1_M-00002-of-00003.gguf`
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- `DeepSeek-V4-Flash-0731-UD-IQ1_M-00003-of-00003.gguf`
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- **HuggingFace repo:** `https://huggingface.co/unsloth/DeepSeek-V4-Flash-0731-GGUF`
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- **Image:** `ghcr.io/ggml-org/llama.cpp:server-vulkan` (floating tag — `deepseek4`
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is a brand-new arch; pin to a specific `server-vulkan-bXXXX` once verified).
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## Hardware target
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NUCBox APU — AMD Ryzen AI Max 395 (Strix Halo), Radeon 8060S (RDNA 3.5),
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~120 GiB unified memory split by firmware into **~90 GiB VRAM** and
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**~30 GiB CPU RAM**. Pinned via `nodeSelector: {kubernetes.io/arch: amd64,
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hardware: high-memory}`.
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## Why this model nearly fills the machine
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The UD-IQ1_M model is **~87 GiB**, almost the entire 90 GiB VRAM pool. Unlike
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the old Qwen models (16–20 GiB, fully offloaded with room to spare), this one
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**cannot be fully offloaded** to the GPU: putting all 43 layers + the KV cache +
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Vulkan compute buffers in VRAM would exceed 90 GiB. The tuning below is all
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about fitting the model while leaving headroom for the KV cache, compute
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buffers, and co-resident pods.
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## Argument-by-argument
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| Flag | Value | Meaning |
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|------|-------|---------|
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| `-m` | `/models/DeepSeek-V4-Flash-0731-UD-IQ1_M-00001-of-00003.gguf` | Model file. Pointed at the **first shard** only; llama.cpp auto-discovers and loads `-00002-…` / `-00003-…` from the same directory. |
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| `--alias` | `deepseek-v4-flash-0731` | Name reported by `GET /v1/models`. Must match the `model_name` in `litellm/litellm.yaml` so LiteLLM routes to this server. |
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| `--host` | `0.0.0.0` | Bind on all interfaces so the k8s Service can reach the pod. |
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| `--port` | `8080` | Listen port (matches `containerPort` + Service `targetPort`). |
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| `--jinja` | *(flag)* | Use the chat template baked into the GGUF (DeepSeek-V4 DSML format, with thinking/reasoning support). Reasoning comes back in `reasoning_content`. |
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| `-ngl` | `40` | **GPU layer offload.** Offload 40 of 43 layers to the GPU. See “VRAM budget” below — full offload would OOM. |
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| `-c` | `65536` | Total KV-cache context window (64k, the required minimum). Single slot gets the full window. |
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| `-np` | `1` | Parallel slots. 1 slot ⇒ the full 64k goes to a single concurrent request. |
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| `--cont-batching` | *(flag)* | Continuous batching across slots (no-op with 1 slot, but harmless and correct if `-np` is raised). |
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| `--cache-type-k` | `q8_0` | Quantize the K cache to q8_0 (halves KV VRAM, ~negligible quality loss). |
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| `--cache-type-v` | `q8_0` | Quantize the V cache to q8_0 (same). |
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| `--temp` | `1.0` | Default sampling temperature (DeepSeek-V4 recommendation). Clients may override per request via the OpenAI API. |
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| `--top-p` | `0.95` | Default nucleus-sampling threshold (DeepSeek-V4 recommendation). Overrideable per request. |
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| `--threads` | `8` | CPU threads for sampling + the 3 CPU-resident layers. Mostly irrelevant under heavy GPU offload. |
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## VRAM budget (90 GiB pool)
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| Component | VRAM |
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|---------------------------------|-------------|
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| Weights (40 GPU layers) | ~81 GiB |
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| KV cache (q8_0, 64k, 1 slot) | ~1.6 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)** | **~5–8 GiB**|
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The remaining **3 layers (~6 GiB) live in CPU RAM** and are counted against the
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pod's cgroup memory limit (not VRAM). KV cache is tiny thanks to DeepSeek-V4's
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**MLA** attention (`num_kv_heads=1`, `head_dim=512` + 64 decoupled RoPE ⇒ ~576
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elements/token/layer); at 64k context q8_0 KV is only ~1.6 GiB, so context is
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**not** the constraint — `-c` is capped at the minimum purely to maximise VRAM
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headroom.
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## How to tune if it OOMs / has spare headroom
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- **Pod OOM-killed or Vulkan out-of-device-memory during load:** lower `-ngl`
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(e.g. `38`) to keep more layers on CPU, or raise the container `memory`
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limit.
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- **VRAM headroom looks generous in logs:** raise `-ngl` toward `43` (full
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offload) and/or raise `-c` for larger context.
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- **Need more concurrent requests:** raise `-np` (KV is cheap), but each extra
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slot multiplies KV VRAM and the context per slot shrinks (`-c / -np`).
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## initContainer (`fetch-model`)
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- Idempotently downloads the 3 shards into the shared models PVC, skipping if
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the first shard is already present (pod restart / recreate).
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- **Deletes the retired Qwen3.6 GGUFs** from the PVC to reclaim space for the
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new 87 GiB model.
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## Resources
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```
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requests: cpu 1000m, memory 4Gi
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limits: cpu 4000m, memory 20Gi
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```
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VRAM (weights + KV + compute) is **not** counted against the cgroup memory
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limit — the limit only covers CPU-side overhead, the mmap'd GGUF pages for the
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3 CPU-resident layers (~6 GiB), and reclaimable page cache during load. k8s
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sees ~30 GiB as the node's allocatable system RAM.
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## Probes
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- `readinessProbe`: `GET /health` after 30s, every 10s, 6 failures.
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- `livenessProbe`: `GET /health` after **300s** (87 GiB load + Vulkan init
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takes several minutes), every 30s, 5 failures.
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## Security / GPU access
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`privileged: true` + mounts `/dev/dri` (DRM render nodes) — simplest reliable
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way to give Vulkan access to the AMD APU on k3s without a device plugin.
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