[https://nvbugs/5634220][fix] Add developer guide back and fix some i… (#8911)

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

Author: nv-guomingz

docs/source/blogs/H100vsA100.md

@@ -28,7 +28,7 @@ TensorRT LLM evaluated on both Hopper and Ampere shows **H100 FP8 is up to 4.6x
 
 <sub>FP8 H100, FP16 A100, SXM 80GB GPUs, TP1, ISL/OSL's provided, TensorRT LLM v0.5.0., TensorRT 9.1</sub>
 
-The full data behind these charts & tables and including larger models with higher TP values can be found in TensorRT LLM's [Performance Documentation](https://nvidia.github.io/TensorRT-LLM/latest/performance/perf-overview.html)
+The full data behind these charts & tables and including larger models with higher TP values can be found in TensorRT LLM's [Performance Documentation](https://nvidia.github.io/TensorRT-LLM/0.21.0/performance/perf-overview.html)
 
 Stay tuned for a highlight on Llama coming soon!
 

docs/source/blogs/H200launch.md

@@ -21,7 +21,7 @@ TensorRT LLM evaluation of the [new H200 GPU](https://nvidianews.nvidia.com/news
 
 <sup>*(1) Largest batch supported on given TP configuration by power of 2.*</sup> <sup>*(2) TP = Tensor Parallelism*</sup>
 
-Additional Performance data is available on the [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference/ai-inference) page, & soon in [TensorRT LLM's Performance Documentation](https://nvidia.github.io/TensorRT-LLM/latest/performance/perf-overview.html).
+Additional Performance data is available on the [NVIDIA Data Center Deep Learning Product Performance](https://developer.nvidia.com/deep-learning-performance-training-inference/ai-inference) page, & soon in [TensorRT LLM's Performance Documentation](https://nvidia.github.io/TensorRT-LLM/0.21.0/performance/perf-overview.html).
 
 ### H200 vs H100
 

docs/source/blogs/tech_blog/blog5_Disaggregated_Serving_in_TensorRT-LLM.md

@@ -124,7 +124,7 @@ In the Dynamo workflow, requests are initially processed by pre- and post-proces
 
 Dynamo also includes built-in support for Kubernetes deployment, monitoring, and metrics collection. The development team is actively working on enabling dynamic instance scaling, further enhancing its suitability for production environments.
 
-For more information on how to use Dynamo with TensorRT LLM, please refer to [this documentation](https://docs.nvidia.com/dynamo/latest/examples/trtllm.html).
+For more information on how to use Dynamo with TensorRT LLM, please refer to [this documentation](https://docs.nvidia.com/dynamo/latest/backends/trtllm/README.html).
 
 ### Triton Inference Server
 

docs/source/features/disagg-serving.md

@@ -186,7 +186,7 @@ In the Dynamo workflow, requests are initially processed by pre- and post-proces
 
 Dynamo also includes built-in support for Kubernetes deployment, monitoring, and metrics collection. The development team is actively working on enabling dynamic instance scaling, further enhancing its suitability for production environments.
 
-For more information on how to use Dynamo with TensorRT-LLM, please refer to [this documentation](https://docs.nvidia.com/dynamo/latest/examples/trtllm.html).
+For more information on how to use Dynamo with TensorRT-LLM, please refer to [this documentation](https://docs.nvidia.com/dynamo/latest/backends/trtllm/README.html).
 
 ## Environment Variables
 

docs/source/index.rst

@@ -74,6 +74,7 @@ Welcome to TensorRT LLM's Documentation!
    features/checkpoint-loading.md
    features/auto_deploy/auto-deploy.md
 
+
 .. toctree::
    :maxdepth: 2
    :caption: Developer Guide

examples/llm-api/extra-llm-api-config.yml

@@ -0,0 +1,5 @@
+cuda_graph_config:
+    enable_padding: True
+    max_batch_size: 16
+moe_config:
+    backend: trtllm

examples/models/core/multimodal/README.md

@@ -901,7 +901,7 @@ Note that for instruct Vision model, please set the `max_encoder_input_len` as `
 
 ## NeVA
 
-[NeVA](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/multimodal/mllm/neva.html) is a groundbreaking addition to the NeMo Multimodal ecosystem. This model seamlessly integrates large language-centric models with a vision encoder, that can be deployed in TensorRT-LLM.
+[NeVA](https://docs.nvidia.com/nemo-framework/user-guide/latest/vlms/neva.html) is a groundbreaking addition to the NeMo Multimodal ecosystem. This model seamlessly integrates large language-centric models with a vision encoder, that can be deployed in TensorRT-LLM.
 
 1. Generate TRT-LLM engine for NVGPT following example in `examples/models/core/gpt/README.md`. To adhere to the NVGPT conventions of the conversion script, some layer keys have to be remapped using `--nemo_rename_key`.
 

examples/sample_weight_stripping/README.md

@@ -241,7 +241,7 @@ python3 ../summarize.py --engine_dir engines/llama2-70b-hf-fp8-tp2.refit \
 
 ## Prototype
 ### Checkpoint Pruner
-The checkpoint pruner allows you to strip `Conv` and `Gemm` weights out of a TensorRT LLM [checkpoint](https://nvidia.github.io/TensorRT-LLM/latest/architecture/checkpoint.html). Since these make up the vast majority of weights, the pruner will decrease the size of your checkpoint up to 99%.
+The checkpoint pruner allows you to strip `Conv` and `Gemm` weights out of a TensorRT LLM [checkpoint](https://nvidia.github.io/TensorRT-LLM/0.21.0/architecture/checkpoint.html). Since these make up the vast majority of weights, the pruner will decrease the size of your checkpoint up to 99%.
 
 When building an engine with a pruned checkpoint, TensorRT LLM fills in the missing weights with random ones. These weights should later be [refit](#engine-refitter) with the original weights to preserve the intended behavior.