From bc1fd80fc0675e2526622fbc7828e7ccd994a34f Mon Sep 17 00:00:00 2001 From: ADou Date: Wed, 9 Sep 2026 10:23:12 +0000 Subject: [PATCH] =?UTF-8?q?[None][docs]=20fix=20VisualGen=20NVL72=20config?= =?UTF-8?q?=20.yml=20=E2=86=92=20.yaml=20in=20blog25?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The NVL72 recipe files live as .yaml on main; blog25 still linked .yml (404). Signed-off-by: ADou --- ..._Video_Generation_Across_NVL72_Rack_with_TensorRT-LLM.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/blogs/tech_blog/blog25_Scaling_Video_Generation_Across_NVL72_Rack_with_TensorRT-LLM.md b/docs/source/blogs/tech_blog/blog25_Scaling_Video_Generation_Across_NVL72_Rack_with_TensorRT-LLM.md index 398dea4054a1..7afbc9b005f9 100644 --- a/docs/source/blogs/tech_blog/blog25_Scaling_Video_Generation_Across_NVL72_Rack_with_TensorRT-LLM.md +++ b/docs/source/blogs/tech_blog/blog25_Scaling_Video_Generation_Across_NVL72_Rack_with_TensorRT-LLM.md @@ -295,7 +295,7 @@ For the full property catalog (every `*_group`, `*_rank`, the flattened `seq_mes ## NVL72 Scaling Results -All performance numbers in this section use the same measured workload and publish one distributed recipe: [`examples/visual_gen/configs/wan22_t2v_bf16_gb200_nvl72.yml`](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/visual_gen/configs/wan22_t2v_bf16_gb200_nvl72.yml). +All performance numbers in this section use the same measured workload and publish one distributed recipe: [`examples/visual_gen/configs/wan22_t2v_bf16_gb200_nvl72.yaml`](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/visual_gen/configs/wan22_t2v_bf16_gb200_nvl72.yaml). | Item | Value | | :--- | :--- | @@ -348,7 +348,7 @@ compilation_config: ### Cosmos3-Super -The same runtime scales a very different model unchanged. Cosmos3-Super is a 64B Mixture-of-Transformers with 64 attention heads, generating a longer 189-frame clip — so each denoising step is an even larger dense prefill than Wan. Only the workload and the per-width recipe change; the CFG, Ulysses, Attention2D, and parallel-VAE knobs are identical ([`examples/visual_gen/configs/cosmos3_t2v_bf16_gb200_nvl72.yml`](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/visual_gen/configs/cosmos3_t2v_bf16_gb200_nvl72.yml)) +The same runtime scales a very different model unchanged. Cosmos3-Super is a 64B Mixture-of-Transformers with 64 attention heads, generating a longer 189-frame clip — so each denoising step is an even larger dense prefill than Wan. Only the workload and the per-width recipe change; the CFG, Ulysses, Attention2D, and parallel-VAE knobs are identical ([`examples/visual_gen/configs/cosmos3_t2v_bf16_gb200_nvl72.yaml`](https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/visual_gen/configs/cosmos3_t2v_bf16_gb200_nvl72.yaml)) | Item | Value | | :--- | :--- | @@ -386,7 +386,7 @@ The same `--visual_gen_args` YAML drives `trtllm-serve` for online serving. Dist export CONTAINER_IMAGE=/path/to/tensorrt-llm.sqsh export PROJECT_ROOT=/path/to/TensorRT-LLM export MODEL=Wan-AI/Wan2.2-T2V-A14B-Diffusers -export SERVER_CONFIG=examples/visual_gen/configs/wan22_t2v_bf16_gb200_nvl72.yml +export SERVER_CONFIG=examples/visual_gen/configs/wan22_t2v_bf16_gb200_nvl72.yaml # Fill in and with your SLURM account and partition sbatch -A -p -N 18 --ntasks-per-node=4 --ntasks=72 examples/visual_gen/serve/benchmark_visual_gen_mgmn_distributed.sh