docs: delete stacks/infinity (retired, replaced by vllm)

Stack was retired and replaced by the vllm stack (originally vllm-qwen3,
renamed 2026-05-13). Its README still framed it as a current solution
while ana-ml2's README + vllm's README both documented the retirement.
stacks/vllm/README.md "Migrating off Infinity" step 3 explicitly said
"Delete stacks/infinity/ from this workspace" — actioning that now.

No backwards-compat shims (PRACTICES §4): contract of a deleted system
has no historical value the next contributor needs; the replacement
path is documented in stacks/vllm/README.md.

Surfaced by /tend-docs audit 2026-05-14.

stacks/infinity/.env.example

@@ -1,30 +0,0 @@
-# Infinity stack tunables. Copy this to `.env` on the server before deploying.
-#
-#   cp .env.example .env
-#   # edit .env with real values
-#   docker compose up -d
-
-# Image version — pin for reproducibility (`latest` for edge)
-INFINITY_VERSION=latest
-
-# Port exposed on host
-INFINITY_PORT=7997
-
-# GPU assignment (ana-ml2 has 0 and 1; default 1 keeps 0 free for heavy LLM work)
-GPU_ID=1
-
-# Models — both served simultaneously; reference by the full repo name in requests
-EMBED_MODEL=Qwen/Qwen3-Embedding-0.6B
-RERANK_MODEL=Qwen/Qwen3-Reranker-0.6B
-
-# Inference engine: torch (widest support) or optimum (ONNX, sometimes faster)
-ENGINE=torch
-
-# Batch size — 32 is a safe default; bump for throughput if VRAM allows
-BATCH_SIZE=32
-
-# Optional API key — leave blank for no auth (fine on the internal network)
-API_KEY=
-
-# HuggingFace token — only needed for gated models
-HF_TOKEN=

stacks/infinity/README.md

@@ -1,65 +0,0 @@
-# infinity
-
-OpenAI-compatible embeddings + reranker server. One container serves both embedding and reranker models simultaneously.
-
-**Server:** ana-ml2
-**Port:** 7997 (infinity default)
-**GPU:** pinned to GPU 1 by default (configurable via `.env`)
-
-## What it replaces / supersedes
-
-- `qwen3-embedding-0.6B` entry in llama-swap (llama.cpp GGUF → infinity transformer)
-- `qwen3-reranker-0.6B` entry in llama-swap
-
-Once infinity is verified stable, retire those two entries from `stacks/llama-swap/config.yaml`.
-
-## Deploy
-
-```bash
-# On ana-ml2:
-sudo mkdir -p /opt/docker/compose/infinity
-sudo chown $USER /opt/docker/compose/infinity
-cd /opt/docker/compose/infinity
-
-# Copy compose.yaml + .env.example here (e.g. via scp from this workspace)
-# Then:
-cp .env.example .env
-# edit .env — pick GPU, models, etc.
-
-# Pre-download models into the shared HF cache (optional, speeds first boot)
-HF_HOME=/tank/aimodels/huggingface hf download "$(grep ^EMBED_MODEL .env | cut -d= -f2)"
-HF_HOME=/tank/aimodels/huggingface hf download "$(grep ^RERANK_MODEL .env | cut -d= -f2)"
-
-# Dry-parse
-docker compose config
-
-# Launch
-docker compose up -d
-docker compose logs -f
-```
-
-## Verify
-
-```bash
-# Health
-curl -s http://localhost:7997/health
-
-# Embedding
-curl -s http://localhost:7997/embeddings \
-  -H "Content-Type: application/json" \
-  -d '{"model":"Qwen/Qwen3-Embedding-0.6B","input":["hello world"]}' | jq .
-
-# Reranker
-curl -s http://localhost:7997/rerank \
-  -H "Content-Type: application/json" \
-  -d '{"model":"Qwen/Qwen3-Reranker-0.6B","query":"what is a cat","documents":["cats are mammals","dogs bark"]}' | jq .
-
-# Listed models
-curl -s http://localhost:7997/models | jq .
-```
-
-## Scaling knobs
-
-- **`BATCH_SIZE`** in `.env` — bigger = higher throughput, more VRAM. 32 is safe; try 64 or 128 if you have headroom.
-- **Model size** — Qwen3-Embedding/Reranker come in 0.6B / 4B / 8B. Pick based on quality-vs-latency tradeoff. On RTX 6000 Ada 46 GB, the 8B pair fits easily (~20 GB VRAM).
-- **`ENGINE=optimum`** — uses ONNX runtime, sometimes faster. Requires the model to have ONNX weights available; fall back to `torch` if it errors on startup.

stacks/infinity/compose.yaml

@@ -1,63 +0,0 @@
-# Infinity — OpenAI-compatible embeddings + reranker server.
-#
-# Serves embedding and reranker models simultaneously from one container
-# on port 7997 (HTTP).  Consumers: AIPA agents (search/retrieval), LibreChat
-# RAG, anything that needs vector embeddings.
-#
-# All tunables live in .env — edit that, not this file.
-#
-# Pre-download models to avoid first-run delay:
-#   HF_HOME=/tank/aimodels/huggingface hf download Qwen/Qwen3-Embedding-0.6B
-#   HF_HOME=/tank/aimodels/huggingface hf download Qwen/Qwen3-Reranker-0.6B
-
-services:
-  infinity:
-    image: michaelf34/infinity:${INFINITY_VERSION}
-    container_name: infinity
-    restart: unless-stopped
-    ports:
-      - "${INFINITY_PORT}:7997"
-    volumes:
-      - /tank/aimodels/huggingface:/hfcache
-    environment:
-      - HF_HOME=/hfcache
-      - HF_HUB_CACHE=/hfcache/hub
-      - HUGGING_FACE_HUB_TOKEN=${HF_TOKEN:-}
-    command: >
-      v2
-      --model-id ${EMBED_MODEL}
-      --model-id ${RERANK_MODEL}
-      --engine ${ENGINE}
-      --device cuda
-      --batch-size ${BATCH_SIZE}
-      --host 0.0.0.0
-      --port 7997
-      --api-key ${API_KEY:-}
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              device_ids:
-                - "${GPU_ID}"
-              capabilities:
-                - gpu
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:7997/health"]
-      interval: 30s
-      timeout: 10s
-      retries: 3
-      start_period: 120s
-    networks:
-      - tnet
-    labels:
-      - homepage.group=AI Systems
-      - homepage.name=Infinity
-      - homepage.icon=mdi-vector-arrange-below
-      - homepage.description=Embeddings + Reranker API (ana-ml2)
-      - homepage.href=http://10.250.50.54:7997/docs
-
-networks:
-  tnet:
-    name: traefik-net
-    external: true