Merge branch 'master' of https://git.d-popov.com/popov/neuron-tai

QUICKSTART.md

@@ -144,11 +144,14 @@ does not need any of this — it is a standard transformer with no FLA fast path
   `python -c "import transformers; print(transformers.__version__)"`.
 - **GPU fast path (optional)** — without it inference still works; startup prints
   `The fast path is not available…` and linear-attention layers use a slower PyTorch
-  fallback. Install **only for your platform**:
+  fallback. **The fast path runs on NVIDIA CUDA GPUs on both Linux and native
+  Windows** — the FLA kernels are Triton-compiled, and `triton-windows` compiles them
+  for CUDA on Windows just like Linux Triton does. Only the pip command differs per
+  platform. Install **only for your platform**:
 
   | Platform | Install | Notes |
   |----------|---------|-------|
-  | **Native Windows + NVIDIA** | `pip install triton-windows` then `pip install flash-linear-attention` | **Fast path works.** FLA [officially supports `triton-windows`](https://github.com/fla-org/flash-linear-attention/pull/757) (tested Win11, PyTorch 2.10, triton-windows 3.6). Do **not** use the `[cuda]` extra on Windows — pip looks for Linux `triton` and fails. Do **not** install `causal-conv1d` — FLA ≥0.3.2 ships Triton conv1d; the separate package is Linux-only and breaks on Windows (`bare_metal_version` / nvcc errors). |
+  | **Native Windows + NVIDIA CUDA** | `pip install triton-windows` then `pip install flash-linear-attention` | **Fast path works on the CUDA GPU** — no CUDA toolkit / `nvcc` needed; `triton-windows` bundles its own compiler. FLA [officially supports `triton-windows`](https://github.com/fla-org/flash-linear-attention/pull/757) (tested Win11, PyTorch 2.10, triton-windows 3.6). Do **not** use the `[cuda]` extra on Windows — that extra only pins Linux PyPI `triton` and fails; it is a packaging name, not a GPU requirement. Do **not** install `causal-conv1d` — FLA ≥0.3.2 ships Triton conv1d; the separate package is Linux-only and breaks on Windows (`bare_metal_version` / nvcc errors). |
   | **Linux + NVIDIA CUDA** | `pip install flash-linear-attention[cuda]` | `causal-conv1d` optional (same FLA built-in conv1d note). Needs CUDA toolkit (`nvcc`) matching torch, or a prebuilt wheel. |
   | **Linux + AMD ROCm** | `pip install flash-linear-attention[rocm]` | Same optional `causal-conv1d` note. |
 

docs/INSTALL_WINDOWS.md

@@ -111,10 +111,10 @@ with `'Qwen3_5MoeConfig' object has no attribute 'vocab_size'`). If you install
 into an existing conda/miniforge env instead of a fresh venv, run
 `pip install -U transformers` there. The startup warning about
 `flash-linear-attention` / `causal-conv1d` ("fast path is not available") is
-harmless on CPU — those are optional CUDA-only kernels.
+harmless on CPU — those are optional GPU kernels.
 
-If you run the node from native Windows instead of WSL2, install the Triton shim
-in the same environment:
+If you run the node from native Windows instead of WSL2, install Triton for
+Windows in the same environment:
 
 ```powershell
 python -m pip install triton-windows
@@ -123,6 +123,12 @@ python -m pip install triton-windows
 Without it, Qwen3.5/3.6-MoE startup can fail with the misleading message
 `Could not import module 'Qwen3_5MoeForCausalLM'`.
 
+**NVIDIA GPU on native Windows:** the CUDA fast path works — after
+`triton-windows`, install FLA with plain `pip install flash-linear-attention`
+(no `[cuda]` extra, no `causal-conv1d`; both are Linux-only packaging and fail
+on Windows). No CUDA toolkit / `nvcc` is needed. See the platform table in
+[QUICKSTART.md](../QUICKSTART.md#qwen3536-moe-notes) for details.
+
 ---
 
 ## Step 6 — Pre-download the model shard

packages/node/meshnet_node/startup.py

@@ -829,7 +829,8 @@ def run_startup(
         shard_label = _format_shard_label(shard_start, shard_end, total_layers)
         public_host = advertise_host or (socket.getfqdn() if host == "0.0.0.0" else host)
         endpoint = f"http://{public_host}:{actual_port}"
-        node.set_advertised_endpoint(endpoint)
+        if hasattr(node, "set_advertised_endpoint"):
+            node.set_advertised_endpoint(endpoint)
         local_base_url = f"http://127.0.0.1:{actual_port}"
         relay_bridge, relay_fields = _start_relay_bridge_if_available(
             tracker_url,
@@ -978,7 +979,8 @@ def run_startup(
         actual_port = node.start()
         public_host = advertise_host or (socket.getfqdn() if host == "0.0.0.0" else host)
         endpoint = f"http://{public_host}:{actual_port}"
-        node.set_advertised_endpoint(endpoint)
+        if hasattr(node, "set_advertised_endpoint"):
+            node.set_advertised_endpoint(endpoint)
         local_base_url = f"http://127.0.0.1:{actual_port}"
         relay_bridge, relay_fields = _start_relay_bridge_if_available(
             tracker_url,
@@ -1156,7 +1158,8 @@ def run_startup(
             shard_label = f"{shard_label} (pinned)"
         public_host = advertise_host or (socket.getfqdn() if host == "0.0.0.0" else host)
         endpoint = f"http://{public_host}:{actual_port}"
-        node.set_advertised_endpoint(endpoint)
+        if hasattr(node, "set_advertised_endpoint"):
+            node.set_advertised_endpoint(endpoint)
         local_base_url = f"http://127.0.0.1:{actual_port}"
         relay_bridge, relay_fields = _start_relay_bridge_if_available(
             tracker_url,