Merge 48fc5eb30e into 6e98abc3b0

README.md

@@ -39,7 +39,7 @@ Each playbook includes prerequisites, step-by-step instructions, troubleshooting
 - [Connect Multiple DGX Spark through a Switch](nvidia/multi-sparks-through-switch/)
 - [NCCL for Two Sparks](nvidia/nccl/)
 - [Fine-tune with NeMo](nvidia/nemo-fine-tune/)
-- [NemoClaw with Nemotron-3-Super and Telegram on DGX Spark](nvidia/nemoclaw/)
+- [NemoClaw with Nemotron 3 Super and Telegram on DGX Spark](nvidia/nemoclaw/)
 - [Nemotron-3-Nano with llama.cpp](nvidia/nemotron/)
 - [NIM on Spark](nvidia/nim-llm/)
 - [NVFP4 Quantization](nvidia/nvfp4-quantization/)

nvidia/nemoclaw/README.md

@@ -1,4 +1,4 @@
-# NemoClaw with Nemotron-3-Super and Telegram on DGX Spark
+# NemoClaw with Nemotron 3 Super and Telegram on DGX Spark
 
 > Install NemoClaw on DGX Spark with local Ollama inference and Telegram bot integration
 
@@ -372,7 +372,15 @@ Open Telegram, find [@BotFather](https://t.me/BotFather), send `/newbot`, and fo
 
 Make sure you are on the **host** (not inside the sandbox). If you are inside the sandbox, run `exit` first.
 
-Add the Telegram network policy to the sandbox so it can reach the Telegram API:
+Set the required environment variables. Replace the placeholders with your actual values. `SANDBOX_NAME` must match the sandbox name you chose during the onboard wizard:
+
+```bash
+export TELEGRAM_BOT_TOKEN=<your-bot-token>
+export SANDBOX_NAME=my-assistant
+export NVIDIA_API_KEY=<your-nvidia-api-key>
+```
+
+Add the Telegram network policy to the sandbox:
 
 ```bash
 nemoclaw my-assistant policy-add
@@ -380,7 +388,7 @@ nemoclaw my-assistant policy-add
 
 When prompted, select `telegram` and hit **Y** to confirm.
 
-Set the bot token and start auxiliary services:
+Start the Telegram bridge.
 
 ```bash
 export TELEGRAM_BOT_TOKEN=<your-bot-token>

nvidia/trt-llm/README.md

@@ -685,6 +685,7 @@ docker rmi ghcr.io/open-webui/open-webui:main
 | "invalid mount config for type 'bind'" | Missing or non-executable entrypoint script | Run `docker inspect <container_id>` to see full error message. Verify `trtllm-mn-entrypoint.sh` exists on both nodes in your home directory (`ls -la $HOME/trtllm-mn-entrypoint.sh`) and has executable permissions (`chmod +x $HOME/trtllm-mn-entrypoint.sh`) |
 | "task: non-zero exit (255)" | Container exit with error code 255 | Check container logs with `docker ps -a --filter "name=trtllm-multinode_trtllm"` to get container ID, then `docker logs <container_id>` to see detailed error messages |
 | Docker state stuck in "Pending" with "no suitable node (insufficien...)" | Docker daemon not properly configured for GPU access | Verify steps 2-4 were completed successfully and check that `/etc/docker/daemon.json` contains correct GPU configuration |
+| Serving model fails `ptxas fatal` errors | Model needs runtime triton kernel compilation | In Step 10, add `-x TRITON_PTXAS_PATH` to your `mpirun` command |
 
 > [!NOTE]
 > DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU.

nvidia/vibe-coding/README.md

@@ -171,10 +171,12 @@ Add additional model entries for any other Ollama models you wish to host remote
 
 | Symptom | Cause | Fix |
 |---------|-------|-----|
-|Ollama not starting|GPU drivers may not be installed correctly|Run `nvidia-smi` in the terminal. If the command fails check DGX Dashboard for updates to your DGX Spark.|
-|Continue can't connect over the network|Port 11434 may not be open or accessible|Run command `ss -tuln \| grep 11434`. If the output does not reflect ` tcp   LISTEN 0      4096               *:11434            *:*  `, go back to step 2 and run the ufw command.|
-|Continue can't detect a locally running Ollama model|Configuration not properly set or detected|Check `OLLAMA_HOST` and `OLLAMA_ORIGINS` in `/etc/systemd/system/ollama.service.d/override.conf` file. If `OLLAMA_HOST` and `OLLAMA_ORIGINS` are set correctly, add these lines to your `~/.bashrc` file.|
-|High memory usage|Model size too big|Confirm no other large models or containers are running with `nvidia-smi`. Use smaller models such as `gpt-oss:20b` for lightweight usage.|
+| **WiFi connection drops or becomes unreachable** (especially in headless mode) | Aggressive WiFi power-saving settings in NetworkManager | Edit `/etc/NetworkManager/conf.d/default-wifi-powersave-on.conf`, set `wifi.powersave = 2`, and run `sudo systemctl restart NetworkManager`. |
+| **Random reboots and "00" error code on the display** | Watchdog timer module (`sbsa_gwdt`) not loaded | Add `sbsa_gwdt` to `/etc/modules-load.d/watchdog.conf` and reboot to ensure the hardware watchdog is correctly managed by the kernel. |
+| Ollama not starting | GPU drivers may not be installed correctly | Run `nvidia-smi` in the terminal. If the command fails check DGX Dashboard for updates to your DGX Spark. |
+| Continue can't connect over the network | Port 11434 may not be open or accessible | Run command `ss -tuln \| grep 11434`. If the output does not reflect `tcp LISTEN 0 4096 *:11434 *:*`, go back to step 2 and run the ufw command. |
+| Continue can't detect a locally running Ollama model | Configuration not properly set or detected | Check `OLLAMA_HOST` and `OLLAMA_ORIGINS` in `/etc/systemd/system/ollama.service.d/override.conf` file. If `OLLAMA_HOST` and `OLLAMA_ORIGINS` are set correctly, add these lines to your `~/.bashrc` file. |
+| High memory usage | Model size too big | Confirm no other large models or containers are running with `nvidia-smi`. Use smaller models such as `gpt-oss:20b` for lightweight usage. |
 
 > [!NOTE]
 > DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU.