Merge 48fc5eb30e into 2022e2b24b

nvidia/speculative-decoding/README.md

@@ -57,7 +57,7 @@ In short: two Sparks let you run models that are too large for one, while specul
 - Docker with GPU support enabled
 
   ```bash
-  docker run --gpus all nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6 nvidia-smi
+  docker run --gpus all nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc12 nvidia-smi
   ```
 - Active HuggingFace Token for model access
 - Network connectivity for model downloads
@@ -68,9 +68,9 @@ In short: two Sparks let you run models that are too large for one, while specul
 * **Duration:** 10-20 minutes for setup, additional time for model downloads (varies by network speed)
 * **Risks:** GPU memory exhaustion with large models, container registry access issues, network timeouts during downloads
 * **Rollback:** Stop Docker containers and optionally clean up downloaded model cache.
-* **Last Updated:** 01/02/2026
-  * Upgrade to latest container v1.2.0rc6
-  * Add EAGLE-3 Speculative Decoding example with GPT-OSS-120B
+* **Last Updated:** 04/20/2026
+  * Upgrade to latest container 1.3.0rc12
+  * Add Speculative Decoding example with Qwen3-235B-A22B on Two Sparks
 
 ## Instructions
 
@@ -111,7 +111,7 @@ docker run \
   -v $HOME/.cache/huggingface/:/root/.cache/huggingface/ \
   --rm -it --ulimit memlock=-1 --ulimit stack=67108864 \
   --gpus=all --ipc=host --network host \
-  nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6 \
+  nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc12 \
   bash -c '
     hf download openai/gpt-oss-120b && \
     hf download nvidia/gpt-oss-120b-Eagle3-long-context \
@@ -172,7 +172,7 @@ docker run \
   -e HF_TOKEN=$HF_TOKEN \
   -v $HOME/.cache/huggingface/:/root/.cache/huggingface/ \
   --rm -it --ulimit memlock=-1 --ulimit stack=67108864 \
-  --gpus=all --ipc=host --network host nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6 \
+  --gpus=all --ipc=host --network host nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc12 \
   bash -c "
 #    # Download models
     hf download nvidia/Llama-3.3-70B-Instruct-FP4 && \
@@ -309,7 +309,7 @@ docker run -d --rm \
   -e TRITON_PTXAS_PATH="/usr/local/cuda/bin/ptxas" \
   -v ~/.cache/huggingface/:/root/.cache/huggingface/ \
   -v ~/.ssh:/tmp/.ssh:ro \
-  nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6 \
+  nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc12 \
   bash -c "curl https://raw.githubusercontent.com/NVIDIA/dgx-spark-playbooks/refs/heads/main/nvidia/trt-llm/assets/trtllm-mn-entrypoint.sh | bash"
 ```
 

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.