feat(2.0): Phase 1 — Engine + Routing (Herzstueck)

Backend-Services: fit/caps/sources (portiert), discover (live HF + Fit +
Caps + ranked recommendation), llama-swap write/register + groups (Ko-
Residenz swap:false), LiteLLM-Gateway-Config + gateway-Service (model:auto +
Fallbacks). Router: discover/fit/register/groups/routing; health zeigt
gateway_reachable. Frontend: Modelle&Routing mit Caps-Chips, Fit-Badges,
Discover-Tab (live), Routing-View.

Lokal verifiziert: Backend-Smoke (alle Endpunkte) + Frontend-Build +
Browser (Shell, Discover, Caps/Fit). Box-Verifikation offen.

Docs: README + docs/STATUS.md (Phasen-Tracker + Resume-Guide).

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

backend/services/fit.py

@@ -0,0 +1,92 @@
+"""
+Hardware-Fit-Mathe (VRAM/RAM, tps-Schätzung) für APUs mit Unified Memory
+(Bosgame M5 / Strix Halo). Portiert aus Mission Control v1 (hw_math.py).
+"""
+
+import re
+
+# Bytes pro Parameter je GGUF-Quant (Annahme).
+QUANT_BYTES_PER_PARAM = {
+    "Q2_K": 0.35, "Q3_K_S": 0.38, "Q3_K_M": 0.42, "Q3_K_L": 0.45,
+    "Q4_0": 0.50, "Q4_1": 0.55, "Q4_K_S": 0.50, "Q4_K_M": 0.55,
+    "Q5_0": 0.62, "Q5_1": 0.68, "Q5_K_S": 0.62, "Q5_K_M": 0.65,
+    "Q6_K": 0.75, "Q8_0": 1.00, "F16": 2.00, "BF16": 2.00,
+}
+
+
+def estimate_memory_gb(params_b: float, quant: str, ctx: int) -> float:
+    """Geschätzter Speicherbedarf in GB (Gewichte + Kontext-KV)."""
+    bpp = QUANT_BYTES_PER_PARAM.get(quant.upper(), 0.65)
+    weights = params_b * bpp
+    context_vram = (ctx / 8192) * (max(params_b, 7) / 7) * 0.8
+    return weights + context_vram
+
+
+def extract_active_params_b(name: str) -> float | None:
+    """Aktive Parameter bei MoE ('30B-A3B' → 3.0). None bei Dense."""
+    m = re.search(r"(?<![a-zA-Z])a(\d+(?:\.\d+)?)b\b", name.lower())
+    return float(m.group(1)) if m else None
+
+
+def estimate_speed(req_gb: float, sys_ram_gb: float, moe_active_ratio: float = 1.0) -> float:
+    """Geschätzte t/s anhand der ~273 GB/s Bandbreite der APU.
+    moe_active_ratio = aktive/gesamt Params; < 1 bei MoE."""
+    bw = 273 if sys_ram_gb > 8 else 70
+    if req_gb <= 0:
+        return 0.0
+    raw_tps = (bw / req_gb) * 0.55
+    if moe_active_ratio < 0.8:
+        raw_tps *= (1.0 / moe_active_ratio) ** 0.5
+    return raw_tps
+
+
+def evaluate_fit(params_b: float, quant: str, ctx: int, sys_ram_gb: float, name: str = "") -> dict:
+    """Fit für ein Shared-Memory-System (APU). name → MoE-Erkennung (optional)."""
+    req_gb = estimate_memory_gb(params_b, quant, ctx)
+    active_b = extract_active_params_b(name) if name else None
+    moe_ratio = (active_b / params_b) if (active_b and params_b > 0) else 1.0
+    tps = estimate_speed(req_gb, sys_ram_gb, moe_ratio)
+    usable_ram = max(sys_ram_gb - 4.0, 0)
+    if req_gb > usable_ram:
+        fit_level, text = "too_tight", "Zu groß (OOM)"
+    elif req_gb > usable_ram * 0.8:
+        fit_level, text = "marginal", "Könnte knapp werden"
+    else:
+        fit_level, text = "perfect", "Passt perfekt"
+    return {"level": fit_level, "text": text, "req_gb": round(req_gb, 1), "tps": round(tps, 0)}
+
+
+def extract_params_b(name: str) -> float:
+    """Parametergröße (Mrd.) aus Repo-/Dateiname. 8x7B (MoE) → 56."""
+    moe = re.search(r"(\d+)x(\d+(?:\.\d+)?)[bB]", name)
+    if moe:
+        return float(moe.group(1)) * float(moe.group(2))
+    m = re.search(r"(\d+(?:\.\d+)?)[bB](?![a-zA-Z])", name)
+    return float(m.group(1)) if m else 7.0
+
+
+_NICE_CTX = [2048, 4096, 8192, 16384, 32768, 49152, 65536, 98304, 131072]
+
+
+def max_ctx_for(params_b: float, quant: str, sys_ram_gb: float) -> int:
+    """Größter 'schöner' Kontext, der komfortabel passt (80 % des nutzbaren RAM)."""
+    bpp = QUANT_BYTES_PER_PARAM.get(quant.upper(), 0.65)
+    weights = params_b * bpp
+    usable = max(sys_ram_gb - 4.0, 0) * 0.8
+    ctx_budget = usable - weights
+    if ctx_budget <= 0:
+        return 2048
+    per_8k = (max(params_b, 7) / 7) * 0.8
+    raw_ctx = (ctx_budget / per_8k) * 8192
+    best = _NICE_CTX[0]
+    for c in _NICE_CTX:
+        if c <= raw_ctx:
+            best = c
+    return best
+
+
+def recommend_ctx(params_b: float, quant: str, sys_ram_gb: float) -> dict:
+    ctx = max_ctx_for(params_b, quant, sys_ram_gb)
+    k = ctx // 1024
+    return {"ctx": ctx, "k": k,
+            "note": f"Bis ~{k}k Kontext passt komfortabel auf deine Hardware ({round(sys_ram_gb)} GB)."}