# Qwen3-235B-A22B-Instruct-2507 (Unsloth UD-Q2_K_XL ~88.8 GB) via the
# kyuz0 rocm-7.2.2 Strix Halo toolbox. Same image + unified-memory
# recipe as compose/llama.yml; the only deltas are model path, port,
# alias, context, and the no-coexist `restart: "no"`.
# https://github.com/kyuz0/amd-strix-halo-toolboxes
#
# Coexistence. At ~88.8 GB weights this CANNOT coexist with the
# 30B llama service or Kimi-Linear (vLLM) — the merged GPU arena is
# only ~110 GB. Stop those before bringing this up. Same pattern as
# compose/comfyui.yml: `restart: "no"`, manual start, swap workflow
# documented in compose/qwen3-235b/README.md.
#
# Weights. UD-Q2_K_XL is Unsloth's "Dynamic" quant — important tensors
# kept at higher precision; closer to Q3 quality than naive Q2. 2 shards
# (~50 GB + 38.8 GB); llama.cpp auto-discovers shard 2 from shard 1.
# Download path on the box (see compose/qwen3-235b/README.md):
#   hf download unsloth/Qwen3-235B-A22B-Instruct-2507-GGUF \
#       --include 'UD-Q2_K_XL/*' \
#       --local-dir /models/qwen/Qwen3-235B-A22B-Instruct-2507
#
# Port 8081 — distinct from llama 30B (8080) so opencode/curl/etc. can
# address either explicitly even though only one runs at a time.
#
# Performance target. Bandwidth-bound: 256 GB/s ÷ ~22 GB active-bytes →
# ~5-10 tok/s decode. This is the "overnight long-task" model, NOT the
# interactive driver — see StrixHaloMemory.md for the bandwidth math.
services:
  qwen3-235b:
    image: kyuz0/amd-strix-halo-toolboxes:rocm-7.2.2
    container_name: qwen3-235b
    # Manual start only — see header note about GPU contention.
    restart: "no"
    devices:
      - /dev/kfd:/dev/kfd
      - /dev/dri:/dev/dri
    cap_add:
      - SYS_PTRACE
    security_opt:
      - seccomp=unconfined
    # Numeric GIDs of host's video (44) and render (991) groups —
    # required for /dev/kfd + /dev/dri access from inside the container.
    group_add:
      - "44"
      - "991"
    shm_size: 8g
    ipc: host
    environment:
      # Unified-memory recipe (same as compose/llama.yml + kimi-linear).
      # BIOS UMA=0.5 GB + ttm.pages_limit cmdline → these flags merge the
      # rocminfo pools into one ~110 GB arena. kyuz0's image is native
      # gfx1151 so no HSA_OVERRIDE_GFX_VERSION.
      - HSA_XNACK=1
      - HSA_FORCE_FINE_GRAIN_PCIE=1
    volumes:
      - /models:/models:ro
    ports:
      - "8081:8081"
    entrypoint: ["llama-server"]
    command:
      - --model
      - /models/qwen/Qwen3-235B-A22B-Instruct-2507/UD-Q2_K_XL/Qwen3-235B-A22B-Instruct-2507-UD-Q2_K_XL-00001-of-00002.gguf
      # OpenAI-compatible served name. Provider-side name lives in
      # opencode.json once M0 perf is verified and we wire it up.
      - --alias
      - qwen3-235b
      - --host
      - 0.0.0.0
      - --port
      - "8081"
      - --n-gpu-layers
      - "999"
      # 64K — opencode auto-compaction triggers at ~75-80 % of the
      # stated context limit, so a small ctx fires the summarize-and-
      # rewind loop after only a few turns. 64K roughly doubles how
      # many turns fit. KV at q8_0 ≈ 8 GB (94 layers × 8 kv-heads × 128
      # head-dim × 2 × 65536 × 1 byte); arena headroom still ~11 GB.
      # Stretch goal 131072 documented in compose/qwen3-235b/README.md
      # but tight — verify allocator behaviour first.
      - --ctx-size
      - "65536"
      - --no-mmap
      # Flash attention is required for q8_0 KV cache in llama.cpp.
      - --flash-attn
      - "on"
      - --cache-type-k
      - q8_0
      - --cache-type-v
      - q8_0
      # Qwen3-235B-Instruct-2507 ships its own chat template; let
      # llama.cpp use it rather than the hardcoded default.
      - --jinja
      # Single sequence — KV pool isn't sliced across speculative
      # concurrent requests we'll never have (long-task model, one
      # request at a time).
      - --parallel
      - "1"
      - --metrics