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chore: Regenerate all playbooks

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14
nvidia/sglang/README.md
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@ -39,7 +39,7 @@ vision-language tasks using models like DeepSeek-V2-Lite.
- NVIDIA Spark device with Blackwell architecture
- Docker Engine installed and running: `docker --version`
- NVIDIA GPU drivers installed: `nvidia-smi`
- NVIDIA Container Toolkit configured: `docker run --rm --gpus all lmsysorg/sglang:spark nvidia-smi`
- NVIDIA Container Toolkit configured: `docker run --rm --gpus all nvcr.io/nvidia/sglang:26.02-py3 nvidia-smi`
- Sufficient disk space (>20GB available): `df -h`
- Network connectivity for pulling NGC containers: `ping nvcr.io`
@ -75,8 +75,8 @@ Note: for NVFP4 models, add the `--quantization modelopt_fp4` flag.
* **Estimated time:** 30 minutes for initial setup and validation
* **Risk level:** Low - Uses pre-built, validated SGLang container with minimal configuration
* **Rollback:** Stop and remove containers with `docker stop` and `docker rm` commands
* **Last Updated:** 01/02/2026
* Add Model Support Matrix
* **Last Updated:** 03/15/2026
* Use latest NGC SGLang container: nvcr.io/nvidia/sglang:26.02-py3
## Instructions
@ -95,7 +95,7 @@ docker --version
nvidia-smi
## Verify Docker GPU support
docker run --rm --gpus all lmsysorg/sglang:spark nvidia-smi
docker run --rm --gpus all nvcr.io/nvidia/sglang:26.02-py3 nvidia-smi
## Check available disk space
df -h /
@ -116,7 +116,7 @@ several minutes depending on your network connection.
```bash
## Pull the SGLang container
docker pull lmsysorg/sglang:spark
docker pull nvcr.io/nvidia/sglang:26.02-py3
## Verify the image was downloaded
docker images | grep sglang
@ -132,7 +132,7 @@ server inside the container, exposing it on port 30000 for client connections.
docker run --gpus all -it --rm \
-p 30000:30000 \
-v /tmp:/tmp \
lmsysorg/sglang:spark \
nvcr.io/nvidia/sglang:26.02-py3 \
bash
```
@ -229,7 +229,7 @@ docker ps | grep sglang | awk '{print $1}' | xargs docker stop
docker container prune -f
## Remove SGLang images (optional)
docker rmi lmsysorg/sglang:spark
docker rmi nvcr.io/nvidia/sglang:26.02-py3
```
## Step 9. Next steps

2
nvidia/trt-llm/README.md
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@ -75,7 +75,7 @@ The following models are supported with TensorRT-LLM on Spark. All listed models
| Model | Quantization | Support Status | HF Handle |
|-------|-------------|----------------|-----------|
| **Nemotron-3-Super-120B** | FP8 | ✅ | `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8` |
| **Nemotron-3-Super-120B** | NVFP4 | ✅ | `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4` |
| **GPT-OSS-20B** | MXFP4 | ✅ | `openai/gpt-oss-20b` |
| **GPT-OSS-120B** | MXFP4 | ✅ | `openai/gpt-oss-120b` |
| **Llama-3.1-8B-Instruct** | FP8 | ✅ | `nvidia/Llama-3.1-8B-Instruct-FP8` |

2
nvidia/vllm/README.md
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@ -53,7 +53,7 @@ The following models are supported with vLLM on Spark. All listed models are ava
| Model | Quantization | Support Status | HF Handle |
|-------|-------------|----------------|-----------|
| **Nemotron-3-Super-120B** | FP8 | ✅ | [`nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8`](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8) |
| **Nemotron-3-Super-120B** | NVFP4 | ✅ | [`nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4`](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4) |
| **GPT-OSS-20B** | MXFP4 | ✅ | [`openai/gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) |
| **GPT-OSS-120B** | MXFP4 | ✅ | [`openai/gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) |
| **Llama-3.1-8B-Instruct** | FP8 | ✅ | [`nvidia/Llama-3.1-8B-Instruct-FP8`](https://huggingface.co/nvidia/Llama-3.1-8B-Instruct-FP8) |