# coder-seat — the fleet's small code-completion model, on nh3-ml1 (CT 109 on # nh3-pve, RTX 2000E Ada, 16 GB). # # Moved here from fv-ml1's `vllm` stack on 2026-09-25 (Prime: utility seats off # the Blackwells). Same model revision, same vLLM version and flags; only the # GPU, the host and the memory fraction changed (the fraction is of a 16 GB card # now, sized to the same ~5.4 GB absolute budget). # # vllm-coder Qwen/Qwen2.5-Coder-1.5B (BASE, FIM) → /v1/completions :8020 # # Consumers: the gateway alias `coder-fast` (ana-docker:4000, # stacks/litellm/conf/config.yaml) and Zed edit-predictions (see README for how # Zed reaches it). # # Shares the card with TEI embed/rerank (stacks/embed-rerank, ~2.6 GB). name: coder-seat services: vllm-coder: image: vllm/vllm-openai:${VLLM_VERSION} container_name: vllm-coder restart: unless-stopped ipc: host ports: - "${CODER_PORT}:8000" volumes: - /opt/aimodels/huggingface:/hfcache environment: - HF_HOME=/hfcache - HF_HUB_CACHE=/hfcache/hub - VLLM_API_KEY=${API_KEY:-} command: - ${CODER_MODEL} # Pinned: the revision fv-ml1 served (and HF main as of 2026-09-25). - --revision - ${CODER_REVISION} - --served-model-name - ${CODER_SERVED_NAME} - --host - 0.0.0.0 - --port - "8000" - --gpu-memory-utilization - ${CODER_GPU_MEM_UTIL} - --max-model-len - ${CODER_MAX_MODEL_LEN} - --max-num-seqs - ${CODER_MAX_NUM_SEQS} - --dtype - auto - --kv-cache-dtype - ${CODER_KV_CACHE_DTYPE} - --enable-prefix-caching deploy: resources: reservations: devices: - driver: nvidia device_ids: ["0"] capabilities: [gpu] healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8000/health"] interval: 30s timeout: 10s retries: 3 start_period: 300s labels: - homepage.group=AI - Inference - homepage.name=vLLM Qwen2.5-Coder 1.5B (FIM, nh3-ml1) - homepage.icon=mdi-code-braces - homepage.description=Qwen2.5-Coder-1.5B FIM code-completion (gateway coder-fast, Zed edit-predictions) - homepage.href=http://10.100.50.80:${CODER_PORT}/docs