[None][doc] add blackwell information into support matrix (NVIDIA#6740)

Signed-off-by: nv-guomingz <[email protected]>
Signed-off-by: Wangshanshan <[email protected]>

Author: nv-guomingz

docs/source/legacy/reference/support-matrix.md

@@ -157,6 +157,7 @@ The following table shows the supported software for TensorRT-LLM.
   - [10.11](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/index.html)
 * - Precision
   -
+    - Blackwell (SM100/SM120) - FP32, FP16, BF16, FP8, FP4, INT8, INT4
     - Hopper (SM90) - FP32, FP16, BF16, FP8, INT8, INT4
     - Ada Lovelace (SM89) - FP32, FP16, BF16, FP8, INT8, INT4
     - Ampere (SM80, SM86) - FP32, FP16, BF16, INT8, INT4[^smgte89]

docs/source/overview.md

@@ -25,8 +25,10 @@ TensorRT LLM delivers breakthrough performance on the latest NVIDIA GPUs:
 
 TensorRT LLM supports the latest and most popular LLM architectures:
 
-- **Language Models**: GPT-OSS, Deepseek-R1/V3, Llama 3/4, Qwen2/3, Gemma 3, Phi 4...
-- **Multi-modal Models**: LLaVA-NeXT, Qwen2-VL, VILA, Llama 3.2 Vision...
+### FP4 Support
+[NVIDIA B200 GPUs](https://www.nvidia.com/en-us/data-center/dgx-b200/) , when used with TensorRT-LLM, enable seamless loading of model weights in the new [FP4 format](https://developer.nvidia.com/blog/introducing-nvfp4-for-efficient-and-accurate-low-precision-inference/#what_is_nvfp4), allowing you to automatically leverage optimized FP4 kernels for efficient and accurate low-precision inference.
+
+### FP8 Support
 
 TensorRT LLM strives to support the most popular models on **Day 0**.