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chore: Regenerate all playbooks

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6
nvidia/trt-llm/README.md
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@ -75,6 +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` |
| **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` |
@ -103,9 +104,8 @@ Reminder: not all model architectures are supported for NVFP4 quantization.
* **Duration**: 45-60 minutes for setup and API server deployment
* **Risk level**: Medium - container pulls and model downloads may fail due to network issues
* **Rollback**: Stop inference servers and remove downloaded models to free resources.
* **Last Updated:** 01/02/2026
* Improve TRT-LLM Run on Two Sparks workflow
* Upgrade to the latest TRT-LLM container v1.2.0rc6
* **Last Updated:** 03/12/2026
* Introduce Nemotron-3-Super-120B support on TRT-LLM
## Single Spark

13
nvidia/vllm/README.md
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@ -53,6 +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) |
| **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) |
@ -87,9 +88,9 @@ Reminder: not all model architectures are supported for NVFP4 quantization.
* **Duration:** 30 minutes for Docker approach
* **Risks:** Container registry access requires internal credentials
* **Rollback:** Container approach is non-destructive.
* **Last Updated:** 01/22/2026
* Added support for Qwen3-VL-Reranker-2B, Qwen3-VL-Reranker-8B, and Qwen3-VL-Embedding-2B models
* Updated container to January 2026 release (26.01-py3)
* **Last Updated:** 03/12/2026
* Added support for Nemotron-3-Super-120B model
* Updated container to Feb 2026 release (26.02-py3)
## Instructions
@ -117,15 +118,11 @@ Find the latest container build from https://catalog.ngc.nvidia.com/orgs/nvidia/
export LATEST_VLLM_VERSION=<latest_container_version>
## example
## export LATEST_VLLM_VERSION=26.01-py3
## export LATEST_VLLM_VERSION=26.02-py3
docker pull nvcr.io/nvidia/vllm:${LATEST_VLLM_VERSION}
```
```bash
docker pull nvcr.io/nvidia/vllm:26.01-py3
```
## Step 3. Test vLLM in container
Launch the container and start vLLM server with a test model to verify basic functionality.