md rework. new code

.claude/memory/project-status.md

@@ -42,7 +42,7 @@ Historical handoff note: `/mnt/c/Users/popov/Downloads/neuron-tai-alpha-handoff-
 - Verification: downloader/startup targeted subset passes (`pytest tests/test_node_startup.py -k "download_shard or same_shard"`). Full `tests/test_node_startup.py` has 46 passed and 4 unrelated Windows chmod/path separator failures.
 - Live Windows confirmation: `meshnet-node start --tracker http://192.168.0.179:8080 --model Qwen3.6-35B-A3B` reuses `F:\_STORAGE\models\qwen3.6-35b-a3b`, prints `Cached at`, registers, and reaches ready as node `5gMLrmyB-26b1f8a4204a`.
 - Follow-up fix: preset-model startup now starts the heartbeat thread after registration; without this, the node appeared briefly on the dashboard and was purged on first inference/route after heartbeat expiry. Tracker dashboard now has a "Console output" panel backed by `/v1/console` for node register/expiry, routing failures, and proxy events.
-- Qwen3.6-35B-A3B reserve-based split is expected: an 79 GB CPU node may be assigned layers 0-36, and a second node fills 37-39. Do not "fix" this by bypassing the 20% assignment reserve unless the shard-planning policy changes.
+- Qwen3.6-35B-A3B CPU runtime cap (2026-07-08): the old reserve-based split could assign an 79 GB CPU node layers 0-36, but real partial loading can exceed that budget and die without a Python traceback. Node startup now clips oversized CPU auto-assignments before loading, and tracker CPU assignment uses a stricter runtime headroom factor; do not revert this to the old 20% reserve-only policy.
 - Route hardening: tracker chat proxy and `/v1/route` diagnostics now use alias-aware preset node matching for split Qwen3.6 routes; dashboard derives grouped inference history from proxy route/complete console events and shows observed TPS after completion.
 - Live proxy hardening: model lookup trims outer whitespace before alias matching (`qwen3.6-35b-a3b ` resolves), and tracker route logs/dashboard queue depth combine heartbeat queue with tracker-local proxy in-flight counts so Postman-style bursts no longer show every selected route as queue `0`.
 - Split-shard streaming hardening: Qwen3.6-style distributed generation now emits SSE chunks token-by-token from the head node instead of buffering all generated text until completion. Tracker direct/relay stream proxy logs `proxy progress` with live tokens/TPS, dashboard Inference history shows currently processing requests with live TPS/tokens/queue, and relay stream completion no longer references an undefined `session_id`.

packages/node/meshnet_node/startup.py

@@ -197,12 +197,43 @@ def _max_assignable_layers(
     memory_mb: int,
     total_layers: int | None,
     bytes_per_layer: int | None = None,
+    *,
+    safety_fraction: float = 0.8,
 ) -> int:
     if total_layers is None or total_layers <= 0 or memory_mb <= 0:
         return 0
     budget_bytes = memory_mb * 1024 * 1024
     layer_bytes = bytes_per_layer or _DEFAULT_BYTES_PER_LAYER
-    return min(total_layers, int((budget_bytes * 0.8) // layer_bytes))
+    return min(total_layers, int((budget_bytes * safety_fraction) // layer_bytes))
+
+
+def _runtime_shard_safety_fraction(device: str) -> float:
+    """CPU partial loads need room for model skeletons, tokenizer, and allocator peaks."""
+    return 0.55 if device != "cuda" else 0.8
+
+
+def _cap_auto_assigned_shard(
+    shard_start: int,
+    shard_end: int,
+    total_layers: int | None,
+    memory_mb: int,
+    bytes_per_layer: int | None,
+    device: str,
+) -> tuple[int, bool, int]:
+    if bytes_per_layer is None or total_layers is None:
+        return shard_end, False, 0
+    max_layers = _max_assignable_layers(
+        memory_mb,
+        total_layers,
+        bytes_per_layer=bytes_per_layer,
+        safety_fraction=_runtime_shard_safety_fraction(device),
+    )
+    if max_layers <= 0:
+        return shard_end, False, max_layers
+    assigned_layers = shard_end - shard_start + 1
+    if assigned_layers <= max_layers:
+        return shard_end, False, max_layers
+    return min(total_layers - 1, shard_start + max_layers - 1), True, max_layers
 
 
 def _format_shard_label(
@@ -226,13 +257,19 @@ def _shard_budget_line(
     total_layers: int | None,
     quantization: str,
     bytes_per_layer: int | None = None,
+    safety_fraction: float = 0.8,
 ) -> str:
     memory_gb = memory_mb / 1024
     gb_str = f"{memory_gb:.1f} GB"
     budget_quantization = "bfloat16" if quantization == "auto" else quantization
     if total_layers is None or total_layers <= 0:
         return f"Memory budget: {gb_str} {memory_source}; shard budget: unknown model layer count"
-    max_layers = _max_assignable_layers(memory_mb, total_layers, bytes_per_layer=bytes_per_layer)
+    max_layers = _max_assignable_layers(
+        memory_mb,
+        total_layers,
+        bytes_per_layer=bytes_per_layer,
+        safety_fraction=safety_fraction,
+    )
     # Remaining capacity after one full model load (rough estimate)
     shard_bytes = max_layers * (bytes_per_layer or _DEFAULT_BYTES_PER_LAYER)
     remaining_gb = (memory_mb * 1024 * 1024 - shard_bytes) / (1024 ** 3)
@@ -729,6 +766,25 @@ def run_startup(
                     if net_asgn.get("hf_repo") == model_id and net_asgn.get("gap_found"):
                         shard_start = net_asgn["shard_start"]
                         shard_end = net_asgn["shard_end"]
+                        asgn_total_layers = int(net_asgn.get("num_layers") or detected)
+                        asgn_bytes_per_layer = _assignment_bytes_per_layer(net_asgn, quantization)
+                        capped_shard_end, was_capped, max_runtime_layers = _cap_auto_assigned_shard(
+                            shard_start,
+                            shard_end,
+                            asgn_total_layers,
+                            memory_budget_mb,
+                            asgn_bytes_per_layer,
+                            device,
+                        )
+                        if was_capped:
+                            original_end = shard_end
+                            shard_end = capped_shard_end
+                            print(
+                                f"  WARNING: tracker assigned layers {shard_start}-{original_end}, "
+                                f"but CPU-safe runtime budget fits {max_runtime_layers}/{asgn_total_layers} layers; "
+                                f"loading layers {shard_start}-{shard_end} instead.",
+                                flush=True,
+                            )
                         full_sources = (
                             [] if tracker_source_disabled
                             else _full_model_sources(net_asgn.get("model_sources", []))
@@ -744,7 +800,7 @@ def run_startup(
                             )
                         print(
                             f"  Tracker found uncovered shard: "
-                            f"layers {shard_start}–{shard_end} (of {detected})",
+                            f"layers {shard_start}-{shard_end} (of {detected})",
                             flush=True,
                         )
                 except Exception:
@@ -859,18 +915,36 @@ def run_startup(
         assigned_shard_start: int = net_assignment["shard_start"]
         assigned_shard_end: int = net_assignment["shard_end"]
         assigned_num_layers: int = net_assignment["num_layers"]
+        assigned_bytes_per_layer = _assignment_bytes_per_layer(net_assignment, quantization)
+        capped_shard_end, was_capped, max_runtime_layers = _cap_auto_assigned_shard(
+            assigned_shard_start,
+            assigned_shard_end,
+            assigned_num_layers,
+            memory_budget_mb,
+            assigned_bytes_per_layer,
+            device,
+        )
+        if was_capped:
+            original_end = assigned_shard_end
+            assigned_shard_end = capped_shard_end
+            print(
+                f"  WARNING: tracker assigned layers {assigned_shard_start}-{original_end}, "
+                f"but CPU-safe runtime budget fits {max_runtime_layers}/{assigned_num_layers} layers; "
+                f"loading layers {assigned_shard_start}-{assigned_shard_end} instead.",
+                flush=True,
+            )
         assigned_model_sources: list[dict] = net_assignment.get("model_sources", [])
         if _gap_found:
             print(
                 f"  Assigned gap: {assigned_hf_repo} "
-                f"layers {assigned_shard_start}–{assigned_shard_end} "
+                f"layers {assigned_shard_start}-{assigned_shard_end} "
                 f"(of {assigned_num_layers})",
                 flush=True,
             )
         else:
             print(
                 f"  Assigned redundant copy: {assigned_hf_repo} "
-                f"layers {assigned_shard_start}–{assigned_shard_end} "
+                f"layers {assigned_shard_start}-{assigned_shard_end} "
                 f"(of {assigned_num_layers})",
                 flush=True,
             )
@@ -956,7 +1030,7 @@ def run_startup(
             f"  Wallet:       {address}\n"
             f"  Model ID:     {assigned_hf_repo}\n"
             f"  Shard:        {shard_label}\n"
-            f"  {_shard_budget_line(memory_budget_mb, memory_budget_source, assigned_num_layers, quantization)}\n"
+            f"  {_shard_budget_line(memory_budget_mb, memory_budget_source, assigned_num_layers, quantization, bytes_per_layer=assigned_bytes_per_layer, safety_fraction=_runtime_shard_safety_fraction(device))}\n"
             f"  Quantization: {quantization}\n"
             f"  Endpoint:     {endpoint}\n"
             f"  Node ID:      {tracker_node_id or 'unregistered'}\n"
@@ -1022,6 +1096,7 @@ def run_startup(
             memory_budget_mb,
             assigned_total_layers,
             assignment_bytes_per_layer,
+            safety_fraction=_runtime_shard_safety_fraction(device),
         )
         if pinned_layers > max_layers:
             raise ValueError(
@@ -1115,7 +1190,7 @@ def run_startup(
             f"  Wallet:       {address}\n"
             f"  Model ID:     {hf_repo}\n"
             f"  Shard:        {shard_label}\n"
-            f"  {_shard_budget_line(memory_budget_mb, memory_budget_source, total_layers, quantization, bytes_per_layer=assignment_bytes_per_layer)}\n"
+            f"  {_shard_budget_line(memory_budget_mb, memory_budget_source, total_layers, quantization, bytes_per_layer=assignment_bytes_per_layer, safety_fraction=_runtime_shard_safety_fraction(device))}\n"
             f"  Quantization: {quantization}\n"
             f"  Endpoint:     {endpoint}\n"
             f"  Node ID:      {tracker_node_id or 'unregistered'}\n"
@@ -1192,7 +1267,7 @@ def run_startup(
         f"meshnet-node ready\n"
         f"  Wallet:   {address}\n"
         f"  Shard:    {shard_label}\n"
-        f"  {_shard_budget_line(memory_budget_mb, memory_budget_source, assigned_total_layers, quantization, bytes_per_layer=assignment_bytes_per_layer)}\n"
+        f"  {_shard_budget_line(memory_budget_mb, memory_budget_source, assigned_total_layers, quantization, bytes_per_layer=assignment_bytes_per_layer, safety_fraction=_runtime_shard_safety_fraction(device))}\n"
         f"  Endpoint: {endpoint}\n"
         f"  Node ID:  {node_id}\n"
         f"  Hardware: {hw_str}\n"

packages/tracker/meshnet_tracker/server.py

@@ -1275,16 +1275,23 @@ def _deployment_summary(nodes: list[_NodeEntry], preset: dict | None) -> dict:
     }
 
 
-def _max_layers_for_memory(memory_mb: int, total_layers: int, preset: dict | None = None) -> int:
+def _max_layers_for_memory(
+    memory_mb: int,
+    total_layers: int,
+    preset: dict | None = None,
+    *,
+    device: str | None = None,
+) -> int:
     if total_layers <= 0:
         return 0
     if memory_mb <= 0:
         return max(1, total_layers // 2)
     memory_bytes = memory_mb * 1024 * 1024
     bytes_per_layer = next(iter(_preset_bytes_per_layer(preset).values())) if preset is not None else 30 * 1024 * 1024
+    safety_fraction = 0.55 if device == "cpu" else 0.8
     return min(
         total_layers,
-        max(1, int((memory_bytes * 0.8) // bytes_per_layer)),
+        max(1, int((memory_bytes * safety_fraction) // bytes_per_layer)),
     )
 
 
@@ -5273,7 +5280,7 @@ class _TrackerHandler(http.server.BaseHTTPRequestHandler):
                 required_start, required_end = _preset_layer_bounds(preset)
                 total_l = required_end - required_start + 1
                 memory_mb = vram_mb if (device == "cuda" and vram_mb > 0) else ram_mb
-                max_layers = _max_layers_for_memory(memory_mb, total_l, preset)
+                max_layers = _max_layers_for_memory(memory_mb, total_l, preset, device=device)
                 shard_start = required_start
                 shard_end = min(required_end, shard_start + max_layers - 1)
                 self._send_json(200, {
@@ -5373,11 +5380,11 @@ class _TrackerHandler(http.server.BaseHTTPRequestHandler):
         memory_mb = vram_mb if (device == "cuda" and vram_mb > 0) else ram_mb
         resolved_name, best_preset = _resolve_model_preset(server.model_presets, str(best_repo))
         if memory_mb > 0:
-            max_layers = _max_layers_for_memory(memory_mb, total_l, best_preset)
+            max_layers = _max_layers_for_memory(memory_mb, total_l, best_preset, device=device)
         elif device == "cuda" and vram_mb >= 8192:
             max_layers = total_l
         else:
-            max_layers = _max_layers_for_memory(memory_mb, total_l, best_preset)
+            max_layers = _max_layers_for_memory(memory_mb, total_l, best_preset, device=device)
 
         shard_start = best_gap_start
         shard_end = min(total_l - 1, shard_start + max_layers - 1)

tests/test_node_startup.py

@@ -1680,6 +1680,66 @@ def test_preset_startup_rejects_pinned_shard_above_memory_budget(tmp_path, monke
         tracker.stop()
 
 
+def test_network_auto_join_clips_oversized_cpu_assignment(tmp_path, monkeypatch, capsys):
+    """Old trackers may assign too many CPU layers; node clips before model load."""
+    import meshnet_node.startup as startup_mod
+
+    torch_calls: list[dict] = []
+    registrations: list[dict] = []
+
+    class FakeBackend:
+        total_layers = 40
+
+    class FakeTorchNodeServer:
+        def __init__(self, **kwargs):
+            torch_calls.append(kwargs)
+            self.backend = FakeBackend()
+            self.tracker_node_id = None
+
+        def start(self):
+            return 7000
+
+        def stop(self):
+            pass
+
+    oversized_assignment = {
+        "hf_repo": "unsloth/Qwen3.6-35B-A3B",
+        "model": "qwen3.6-35b-a3b",
+        "shard_start": 0,
+        "shard_end": 36,
+        "num_layers": 40,
+        "gap_found": False,
+        "bytes_per_layer": {"bfloat16": 1_797_594_419},
+        "model_sources": [],
+    }
+
+    monkeypatch.setattr(
+        startup_mod,
+        "detect_hardware",
+        lambda: {"device": "cpu", "gpu_name": None, "vram_mb": 0, "ram_mb": 79 * 1024},
+    )
+    monkeypatch.setattr(startup_mod, "TorchNodeServer", FakeTorchNodeServer)
+    monkeypatch.setattr(startup_mod, "_get_json", lambda *_args, **_kwargs: oversized_assignment)
+    monkeypatch.setattr(startup_mod, "_post_json", lambda _url, payload: registrations.append(payload) or {"node_id": "n1"})
+    monkeypatch.setattr(startup_mod, "_start_heartbeat", lambda *_args, **_kwargs: None)
+    monkeypatch.setattr(startup_mod, "model_metadata_for", lambda *_args, **_kwargs: {"num_layers": 40})
+
+    node = run_startup(
+        tracker_url="http://127.0.0.1:8080",
+        wallet_path=tmp_path / "wallet.json",
+        tracker_source_disabled=True,
+    )
+    try:
+        assert torch_calls[0]["shard_start"] == 0
+        assert torch_calls[0]["shard_end"] == 24
+        assert registrations[0]["shard_end"] == 24
+        output = capsys.readouterr().out
+        assert "CPU-safe runtime budget fits 25/40 layers" in output
+        assert "layers 0-24" in output
+    finally:
+        node.stop()
+
+
 def test_preset_model_with_hf_repo_loads_torch_backend(tmp_path, monkeypatch, capsys):
     """Named presets that advertise hf_repo must load TorchNodeServer, not the stub server."""
     import meshnet_node.startup as startup_mod
@@ -1996,6 +2056,55 @@ def test_network_assign_gap_found_field():
         tracker.stop()
 
 
+def test_network_assign_uses_conservative_cpu_runtime_budget():
+    """CPU assignments leave headroom for partial-load overhead, not just raw weights."""
+    import json as _json
+    import urllib.request as _ur
+
+    tracker = TrackerServer(model_presets={
+        "qwen3.6-35b-a3b": {
+            "hf_repo": "unsloth/Qwen3.6-35B-A3B",
+            "aliases": ["unsloth/Qwen3.6-35B-A3B"],
+            "layers_start": 0,
+            "layers_end": 39,
+            "recommended": True,
+            "bytes_per_layer": {"bfloat16": 1_797_594_419},
+        },
+    })
+    port = tracker.start()
+    try:
+        data = _json.dumps({
+            "endpoint": "http://127.0.0.1:9200",
+            "model": "qwen3.6-35b-a3b",
+            "hf_repo": "unsloth/Qwen3.6-35B-A3B",
+            "num_layers": 40,
+            "shard_start": 0,
+            "shard_end": 39,
+            "hardware_profile": {},
+            "score": 1.0,
+        }).encode()
+        req = _ur.Request(
+            f"http://127.0.0.1:{port}/v1/nodes/register",
+            data=data,
+            headers={"Content-Type": "application/json"},
+            method="POST",
+        )
+        with _ur.urlopen(req) as r:
+            r.read()
+
+        resp = _get_json(
+            f"http://127.0.0.1:{port}/v1/network/assign"
+            "?device=cpu&vram_mb=0&ram_mb=80896"
+            "&hf_repo=unsloth/Qwen3.6-35B-A3B"
+        )
+
+        assert resp["gap_found"] is False
+        assert resp["shard_start"] == 0
+        assert resp["shard_end"] == 24
+    finally:
+        tracker.stop()
+
+
 def test_route_finds_hf_model_across_two_nodes():
     """Tracker /v1/route returns ordered route for HF model even without a preset."""
     import json as _json