fleet redo 2026-08-17: retire PP+NPU, new topology

- Move all previous scripts to old/scripts/ (retired PP/NPU-era)
- frank-default-large: llama.cpp FastMTP on dankserver (HauhauCS
  Qwen3.8-27B IQ4_XS + FastMTP 32K draft + ngram-mod, K q8_0/V f16,
  128K ctx, batch 2048/ubatch 1024, port 7770)
- frank-default-small: vLLM ROCm on dankclaw (Avesed Qwen3.6-35B-A3B
  INT8 W8A8, full 262144 ctx, no KV quant, 4 seqs, batch 16384,
  ngram spec, port 52625)
- retarget deploy.sh (dankserver/dankclaw, inline sudo, no helpers)
- docs: deployment + index + AGENTS.md + README for new topology,
  superseded banners on retired docs, add frank-redo-2026-08-17 runbook

old/scripts/serve-vllm-qwen36-dflash.sh

@@ -0,0 +1,56 @@
+#!/usr/bin/env bash
+# Serve Qwen3.6-35B-A3B-FP8 with DFlash speculative decoding via vLLM on NVIDIA RTX 3090
+# Uses Podman container with CUDA support
+# 4 parallel streams, full context (262K) each
+set -euo pipefail
+
+# Configuration
+MODEL="${MODEL:-Qwen/Qwen3.6-35B-A3B-FP8}"
+DRAFT_MODEL="${DRAFT_MODEL:-z-lab/Qwen3.6-35B-A3B-DFlash}"
+PORT="${PORT:-8000}"
+GPU_MEM_UTIL="${GPU_MEM_UTIL:-0.95}"
+MAX_NUM_SEQS="${MAX_NUM_SEQS:-4}"
+MAX_MODEL_LEN="${MAX_MODEL_LEN:-262144}"
+KV_CACHE_DTYPE="${KV_CACHE_DTYPE:-fp8}"
+NUM_SPEC_TOKENS="${NUM_SPEC_TOKENS:-8}"
+CONTAINER_NAME="${CONTAINER_NAME:-vllm-qwen36-dflash}"
+HF_CACHE="${HF_CACHE:-/home/dank/.cache/huggingface}"
+GPU_LIBS="${GPU_LIBS:-/usr/lib/x86_64-linux-gnu}"
+
+echo "Starting vLLM server in Podman..."
+echo "  Target model: ${MODEL}"
+echo "  Draft model:  ${DRAFT_MODEL}"
+echo "  Port:         ${PORT}"
+echo "  GPU mem util: ${GPU_MEM_UTIL}"
+echo "  Max seqs:     ${MAX_NUM_SEQS}"
+echo "  Context len:  ${MAX_MODEL_LEN}"
+echo "  KV cache:     ${KV_CACHE_DTYPE}"
+echo "  Spec tokens:  ${NUM_SPEC_TOKENS}"
+echo ""
+
+# Remove existing container if present
+podman rm -f "${CONTAINER_NAME}" 2>/dev/null || true
+
+# Run vLLM in Podman container (foreground mode for systemd)
+# Note: vllm/vllm-openai image has entrypoint "vllm serve", so we pass args directly
+exec podman run --name "${CONTAINER_NAME}" \
+  --device /dev/nvidia0 \
+  --device /dev/nvidiactl \
+  --device /dev/nvidia-uvm \
+  --device /dev/nvidia-uvm-tools \
+  --device /dev/nvidia-modeset \
+  -v "${GPU_LIBS}:/usr/lib/x86_64-linux-gnu:ro" \
+  -v "${HF_CACHE}:/root/.cache/huggingface" \
+  -p "${PORT}:${PORT}" \
+  docker.io/vllm/vllm-openai:latest \
+  "${MODEL}" \
+    --port "${PORT}" \
+    --host "0.0.0.0" \
+    --kv-cache-dtype "${KV_CACHE_DTYPE}" \
+    --gpu-memory-utilization "${GPU_MEM_UTIL}" \
+    --max-num-seqs "${MAX_NUM_SEQS}" \
+    --max-model-len "${MAX_MODEL_LEN}" \
+    --spec-method dflash \
+    --spec-model "${DRAFT_MODEL}" \
+    --spec-tokens "${NUM_SPEC_TOKENS}" \
+    --trust-remote-code