feat(models): skip install-time pull; hardware-aware picks in Models tab

Windows installer no longer auto-downloads models; points to the Models tab.
synapse/hardware.py detects RAM + best-effort VRAM and a curated catalog;
GET /models/recommended annotates each model with fit (gpu/ram/no); the Models
page shows detected RAM/VRAM with fit badges and per-row Pull buttons.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

synapse/hardware.py

@@ -0,0 +1,81 @@
+"""Hardware detection + a curated model catalog for the Models page.
+
+Detects system RAM (reliable, psutil) and GPU VRAM (best-effort: nvidia-smi,
+then Linux AMD sysfs). VRAM is None when it can't be read (e.g. AMD/Vulkan on
+Windows) — the UI then recommends by RAM + model size alone.
+"""
+from __future__ import annotations
+
+import glob
+import subprocess
+from typing import Any, Dict, List, Optional
+
+# Curated local-friendly models with their Q4 on-disk sizes (GB) and role.
+# Sizes are approximate default-quant download sizes.
+CATALOG: List[Dict[str, Any]] = [
+    {"name": "gemma2:2b",       "size_gb": 1.6, "params": "2B",   "role": "chat",       "note": "Smallest; fast on any GPU"},
+    {"name": "llama3.2:3b",     "size_gb": 2.0, "params": "3B",   "role": "chat",       "note": "Small Llama, fits 4GB GPU"},
+    {"name": "qwen3:4b",        "size_gb": 2.5, "params": "4B",   "role": "chat",       "note": "Reasoning (Think toggle); great on a 4GB card"},
+    {"name": "phi3:mini",       "size_gb": 2.2, "params": "3.8B", "role": "chat",       "note": "Strong for its size"},
+    {"name": "mistral:latest",  "size_gb": 4.1, "params": "7B",   "role": "chat/memory","note": "Good curator; runs on CPU/8GB+ RAM"},
+    {"name": "qwen2.5:7b",      "size_gb": 4.7, "params": "7B",   "role": "chat",       "note": "Capable 7B"},
+    {"name": "llama3.1:8b",     "size_gb": 4.7, "params": "8B",   "role": "chat",       "note": "Higher quality; CPU or 6GB+ GPU"},
+    {"name": "nomic-embed-text","size_gb": 0.27,"params": "—",    "role": "embeddings", "note": "Required for recall / document RAG"},
+]
+
+
+def _ram_gb() -> Optional[float]:
+    try:
+        import psutil
+        return round(psutil.virtual_memory().total / 1024 ** 3, 1)
+    except Exception:
+        return None
+
+
+def _vram_gb() -> Optional[float]:
+    # NVIDIA
+    try:
+        r = subprocess.run(
+            ["nvidia-smi", "--query-gpu=memory.total", "--format=csv,noheader,nounits"],
+            capture_output=True, text=True, timeout=3,
+        )
+        if r.returncode == 0 and r.stdout.strip():
+            mb = max(int(x) for x in r.stdout.split())
+            return round(mb / 1024, 1)
+    except Exception:
+        pass
+    # Linux AMD/Intel via DRM sysfs
+    try:
+        best = 0
+        for p in glob.glob("/sys/class/drm/card*/device/mem_info_vram_total"):
+            try:
+                best = max(best, int(open(p).read().strip()))
+            except Exception:
+                pass
+        if best:
+            return round(best / 1024 ** 3, 1)
+    except Exception:
+        pass
+    return None  # unknown (e.g. AMD/Vulkan on Windows) -> recommend by RAM
+
+
+def detect() -> Dict[str, Any]:
+    return {"ram_gb": _ram_gb(), "vram_gb": _vram_gb()}
+
+
+def _fit(size_gb: float, vram: Optional[float], ram: Optional[float]) -> str:
+    """Where a model can run: 'gpu' (fully resident), 'ram' (CPU), or 'no'."""
+    if vram and size_gb + 1.0 <= vram:      # ~1GB headroom for KV cache/context
+        return "gpu"
+    if ram and size_gb + 2.0 <= ram:        # ~2GB headroom for the OS/app
+        return "ram"
+    return "no"
+
+
+def recommend() -> Dict[str, Any]:
+    hw = detect()
+    models = [
+        {**m, "fit": _fit(m["size_gb"], hw["vram_gb"], hw["ram_gb"])}
+        for m in CATALOG
+    ]
+    return {"hardware": hw, "models": models}

synapse/main.py

@@ -621,6 +621,13 @@ async def reorder_memory(payload: Dict[str, Any] = Body(...)):
 # -------------------------
 # Models
 # -------------------------
+@app.get("/models/recommended")
+async def models_recommended():
+    """Curated models annotated with whether they fit this machine's VRAM/RAM."""
+    from . import hardware
+    return hardware.recommend()
+
+
 @app.get("/models")
 async def get_models():
     try: