[OMNIML-3036][doc] Re-branding TensorRT-Model-Optimizer as Nvidia Model-Optimizer

[cjluo-nv]

Summary by CodeRabbit

Release Notes

• Documentation

  • Updated tool and product references throughout documentation and examples
  • Corrected repository links and URLs to point to current source locations
  • Standardized naming conventions across documentation, blog posts, and example files
  • Updated license attributions and references for third-party libraries

• Chores

  • Updated attribution files with corrected licensing information

_{✏️ Tip: You can customize this high-level summary in your review settings.}

Description

Test Coverage

PR Checklist

Please review the following before submitting your PR:

• PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.

• PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.

• Test cases are provided for new code paths (see test instructions)

• Any new dependencies have been scanned for license and vulnerabilities

• CODEOWNERS updated if ownership changes

• Documentation updated as needed

• Update tava architecture diagram if there is a significant design change in PR.

• The reviewers assigned automatically/manually are appropriate for the PR.

• Please check this after reviewing the above items as appropriate for this PR.

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[coderabbitai]

📝 Walkthrough

Walkthrough

The changes update documentation, attribution files, and comments across the repository to reflect a naming change from "TensorRT Model Optimizer" to "Model Optimizer" and corresponding repository URL updates from NVIDIA/TensorRT-Model-Optimizer to NVIDIA/Model-Optimizer. No functional code or API changes are introduced.

Changes

Cohort / File(s)	Summary
Attribution & Metadata ATTRIBUTIONS-Python.md	Updated URLs and license references for third-party libraries, replacing TensorRT-related GitHub links with Model-Optimizer equivalents
Root Documentation README.md	Rebranded three news items, removing "TensorRT" qualifier from "Model Optimizer" references
Technical Blogs docs/source/blogs/tech_blog/blog1_*, blog3_*, blog14_*	Updated terminology and links from "NVIDIA TensorRT Model Optimizer" to "NVIDIA Model Optimizer" in checkpoint attribution text; added Hugging Face model links in one blog
Developer Guides docs/source/developer-guide/perf-benchmarking.md, perf-overview.md	Updated external documentation links and example references from TensorRT Model Optimizer to Model Optimizer
Feature Documentation docs/source/features/auto_deploy/support_matrix.md, quantization.md	Updated tool references, repository paths, and links from TensorRT-Model-Optimizer to Model-Optimizer throughout quantization and deployment guidance
Legacy Documentation docs/source/legacy/performance/perf-benchmarking.md, docs/source/torch/auto_deploy/support_matrix.md, docs/source/torch/features/quantization.md	Updated checkpoint source naming and repository references to reflect Model Optimizer branding
Example READMEs — LLM & Deployment examples/auto_deploy/README.md, disaggregated/README.md, llm-api/_tensorrt_engine/*, medusa/README.md	Updated section headings, prerequisites, and checkpoint descriptions from TensorRT Model Optimizer to Model Optimizer nomenclature
Example READMEs — Model-Specific examples/models/core/deepseek_v3/README.md, exaone/README.md, llama/README.md, llama4/README.md, qwen/README.md	Updated ModelOpt repository clone commands, default checkpoint paths, and references from TensorRT-Model-Optimizer to Model-Optimizer across quantization and benchmark sections
Example Python Comments examples/llm-api/llm_inference.py, quickstart_example.py, examples/llm-api/_tensorrt_engine/quickstart_example.py, llm_medusa_decoding.py	Updated help strings and code comments referencing "TensorRT Model Optimizer" checkpoints to "Model Optimizer"
Example Installation Docs examples/quantization/README.md	Updated installation language from "NVIDIA TensorRT Model Optimizer quantization toolkit" to "NVIDIA Model Optimizer quantization toolkit"

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

• Rationale: Highly homogeneous changes across ~30 files following a single consistent pattern (terminology and URL replacement). Review effort can focus on spot-checking representative files from each cohort to confirm the pattern is applied correctly rather than evaluating unique logic in each file.
• Areas for attention:
  • Verify all repository URLs correctly reference NVIDIA/Model-Optimizer instead of NVIDIA/TensorRT-Model-Optimizer
  • Confirm no unintended text deletions or partial replacements occurred in longer documentation sections (e.g., docs/source/features/quantization.md, example READMEs)

Pre-merge checks and finishing touches

❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.
Description check	⚠️ Warning	The PR description is entirely the template with no actual content filled in; no description of changes, test coverage, or checklist confirmations provided.	Fill in the Description section explaining the rebranding rationale, add Test Coverage section, and check off relevant checklist items to confirm the changes meet repository standards.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The PR title accurately describes the main change: rebranding references from 'TensorRT-Model-Optimizer' to 'Model Optimizer' across documentation and examples.

✨ Finishing touches

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• Post copyable unit tests in a comment

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[coderabbitai]

Actionable comments posted: 1

Caution

Some comments are outside the diff and can’t be posted inline due to platform limitations.

⚠️ Outside diff range comments (1)

docs/source/torch/features/quantization.md (1)

4-17: Update the repository URL to include the "TensorRT-" prefix.

The git clone command references https://github.com/NVIDIA/Model-Optimizer, but the correct repository is https://github.com/NVIDIA/TensorRT-Model-Optimizer. The examples/llm_ptq directory does exist in the correct repository, but users will encounter a 404 error with the current URL.

🧹 Nitpick comments (2)

examples/models/core/llama4/README.md (1)

45-45: Capitalize “NVIDIA Model Optimizer” consistently

The rebranding looks correct, but here “nvidia Model Optimizer” is lower‑case while other docs use “NVIDIA Model Optimizer”. Consider updating these sentences to “TensorRT LLM supports NVIDIA Model Optimizer quantized FP8 checkpoint(s)” for consistency and branding clarity.

Also applies to: 97-97, 143-143

docs/source/torch/auto_deploy/support_matrix.md (1)

121-121: Align visible product name with repository link

To match other docs, you could phrase this as “NVIDIA Model Optimizer” while still linking to the NVIDIA/Model-Optimizer repo, e.g.:

quantized checkpoints generated by NVIDIA Model Optimizer.

Purely editorial; current text is clear.

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Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between a1964bc and 6d1ad01.

⛔ Files ignored due to path filters (2)

• security_scanning/examples/models/core/mllama/poetry.lock is excluded by !**/*.lock
• security_scanning/poetry.lock is excluded by !**/*.lock

📒 Files selected for processing (25)

• ATTRIBUTIONS-Python.md (2 hunks)
• README.md (2 hunks)
• docs/source/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md (1 hunks)
• docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md (1 hunks)
• docs/source/blogs/tech_blog/blog3_Optimizing_DeepSeek_R1_Throughput_on_NVIDIA_Blackwell_GPUs.md (1 hunks)
• docs/source/developer-guide/perf-benchmarking.md (1 hunks)
• docs/source/developer-guide/perf-overview.md (1 hunks)
• docs/source/features/auto_deploy/support_matrix.md (1 hunks)
• docs/source/features/quantization.md (3 hunks)
• docs/source/legacy/performance/perf-benchmarking.md (1 hunks)
• docs/source/torch/auto_deploy/support_matrix.md (1 hunks)
• docs/source/torch/features/quantization.md (2 hunks)
• examples/auto_deploy/README.md (1 hunks)
• examples/disaggregated/README.md (1 hunks)
• examples/llm-api/_tensorrt_engine/llm_medusa_decoding.py (2 hunks)
• examples/llm-api/_tensorrt_engine/quickstart_example.py (1 hunks)
• examples/llm-api/llm_inference.py (1 hunks)
• examples/llm-api/quickstart_example.py (1 hunks)
• examples/medusa/README.md (1 hunks)
• examples/models/core/deepseek_v3/README.md (2 hunks)
• examples/models/core/exaone/README.md (2 hunks)
• examples/models/core/llama/README.md (1 hunks)
• examples/models/core/llama4/README.md (3 hunks)
• examples/models/core/qwen/README.md (3 hunks)
• examples/quantization/README.md (1 hunks)

🧰 Additional context used

📓 Path-based instructions (2)

**/*.py

📄 CodeRabbit inference engine (CODING_GUIDELINES.md)

**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+
Indent Python code with 4 spaces; do not use tabs
Always maintain the namespace when importing in Python, even if only one class or function from a module is used (e.g., use from package.subpackage import foo and then foo.SomeClass() instead of from package.subpackage.foo import SomeClass)
Python filenames should use snake_case (e.g., some_file.py)
Python class names should use PascalCase (e.g., class SomeClass)
Python function and method names should use snake_case (e.g., def my_awesome_function():)
Python local variable names should use snake_case, with prefix k for variable names that start with a number (e.g., k_99th_percentile = ...)
Python global variables should use upper snake_case with prefix G (e.g., G_MY_GLOBAL = ...)
Python constants should use upper snake_case (e.g., MY_CONSTANT = ...)
Avoid shadowing variables declared in an outer scope in Python
Initialize all externally visible members of a Python class in the constructor
For Python interfaces that may be used outside a file, prefer docstrings over comments
Python comments should be reserved for code within a function, or interfaces that are local to a file
Use Google style docstrings for Python classes and functions, which can be parsed by Sphinx
Python attributes and variables can be documented inline with type and description (e.g., self.x = 5 followed by """<type>: Description of 'x'""" )
Avoid using reflection in Python when functionality can be easily achieved without reflection
When using try-except blocks in Python, limit the except clause to the smallest set of specific errors possible instead of catching all exceptions
When using try-except blocks in Python to handle multiple possible variable types (duck-typing), keep the body of the try as small as possible and use the else block to implement the logic

Files:

• examples/llm-api/quickstart_example.py
• examples/llm-api/llm_inference.py
• examples/llm-api/_tensorrt_engine/quickstart_example.py
• examples/llm-api/_tensorrt_engine/llm_medusa_decoding.py

**/*.{cpp,h,cu,py}

📄 CodeRabbit inference engine (CODING_GUIDELINES.md)

All TensorRT-LLM Open Source Software code files should contain an NVIDIA copyright header that includes the current year at the top

Files:

• examples/llm-api/quickstart_example.py
• examples/llm-api/llm_inference.py
• examples/llm-api/_tensorrt_engine/quickstart_example.py
• examples/llm-api/_tensorrt_engine/llm_medusa_decoding.py

🧠 Learnings (34)

📓 Common learnings

Learnt from: venkywonka
Repo: NVIDIA/TensorRT-LLM PR: 6029
File: .github/pull_request_template.md:45-53
Timestamp: 2025-08-27T17:50:13.264Z
Learning: For PR templates in TensorRT-LLM, avoid suggesting changes that would increase developer overhead, such as converting plain bullets to mandatory checkboxes. The team prefers guidance-style bullets that don't require explicit interaction to reduce friction in the PR creation process.

Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4010-4012
Timestamp: 2025-08-14T23:23:27.449Z
Learning: For MOE (Mixture of Experts) code reviews in TensorRT-LLM, avoid repeatedly suggesting finalize fusion validation checks and safety assertions. The user djns99 has indicated these suggestions are repetitive and unwanted across multiple MOE-related changes.

📚 Learning: 2025-08-09T02:04:49.623Z

Learnt from: Fridah-nv
Repo: NVIDIA/TensorRT-LLM PR: 6760
File: tensorrt_llm/_torch/auto_deploy/models/quant_config_reader.py:81-98
Timestamp: 2025-08-09T02:04:49.623Z
Learning: In TensorRT-LLM's auto_deploy module, torch.dtype values in configuration dictionaries must be stored as string representations (e.g., "float16" instead of torch.float16) because OmegaConf.merge does not support torch.dtype types. These string representations are converted to actual torch.dtype objects in downstream code.

Applied to files:

• docs/source/features/auto_deploy/support_matrix.md
• docs/source/torch/auto_deploy/support_matrix.md
• examples/models/core/exaone/README.md

📚 Learning: 2025-08-21T00:16:56.457Z

Learnt from: farshadghodsian
Repo: NVIDIA/TensorRT-LLM PR: 7101
File: docs/source/blogs/tech_blog/blog9_Deploying_GPT_OSS_on_TRTLLM.md:36-36
Timestamp: 2025-08-21T00:16:56.457Z
Learning: TensorRT-LLM container release tags in documentation should only reference published NGC container images. The README badge version may be ahead of the actual published container versions.

Applied to files:

• examples/disaggregated/README.md
• docs/source/legacy/performance/perf-benchmarking.md
• examples/models/core/qwen/README.md
• docs/source/developer-guide/perf-benchmarking.md
• docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md
• examples/models/core/llama/README.md
• docs/source/torch/features/quantization.md
• docs/source/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md
• examples/models/core/llama4/README.md
• examples/llm-api/_tensorrt_engine/quickstart_example.py
• README.md

📚 Learning: 2025-09-09T09:40:45.658Z

Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
Learning: In TensorRT-LLM test lists, it's common and intentional for the same test to appear in multiple test list files when they serve different purposes (e.g., llm_function_core.txt for comprehensive core functionality testing and llm_function_core_sanity.txt for quick sanity checks). This duplication allows tests to be run in different testing contexts.

Applied to files:

• docs/source/legacy/performance/perf-benchmarking.md
• docs/source/developer-guide/perf-benchmarking.md
• examples/models/core/deepseek_v3/README.md
• examples/models/core/llama/README.md
• docs/source/features/quantization.md
• examples/models/core/llama4/README.md

📚 Learning: 2025-08-26T09:37:10.463Z

Learnt from: jiaganc
Repo: NVIDIA/TensorRT-LLM PR: 7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM's bench configuration, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which is a Dict[str, Any] that can contain default values including `cuda_graph_config`, making the fallback `llm_args["cuda_graph_config"]` safe to use.

Applied to files:

• docs/source/legacy/performance/perf-benchmarking.md
• docs/source/developer-guide/perf-benchmarking.md
• examples/models/core/deepseek_v3/README.md
• examples/llm-api/_tensorrt_engine/quickstart_example.py

📚 Learning: 2025-08-06T13:58:07.506Z

Learnt from: galagam
Repo: NVIDIA/TensorRT-LLM PR: 6487
File: tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py:1-12
Timestamp: 2025-08-06T13:58:07.506Z
Learning: In TensorRT-LLM, test files (files under tests/ directories) do not require NVIDIA copyright headers, unlike production source code files. Test files typically start directly with imports, docstrings, or code.

Applied to files:

• docs/source/legacy/performance/perf-benchmarking.md
• docs/source/developer-guide/perf-benchmarking.md
• examples/quantization/README.md
• examples/models/core/deepseek_v3/README.md
• examples/models/core/llama/README.md
• examples/medusa/README.md
• docs/source/features/quantization.md
• examples/llm-api/quickstart_example.py
• examples/models/core/llama4/README.md
• examples/models/core/exaone/README.md
• examples/llm-api/_tensorrt_engine/quickstart_example.py

📚 Learning: 2025-07-28T17:06:08.621Z

Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.

Applied to files:

• docs/source/legacy/performance/perf-benchmarking.md
• docs/source/developer-guide/perf-benchmarking.md
• examples/auto_deploy/README.md
• examples/models/core/deepseek_v3/README.md
• examples/models/core/llama/README.md
• docs/source/torch/features/quantization.md
• docs/source/features/quantization.md
• examples/llm-api/quickstart_example.py
• examples/models/core/llama4/README.md
• examples/models/core/exaone/README.md
• examples/llm-api/llm_inference.py
• examples/llm-api/_tensorrt_engine/quickstart_example.py

📚 Learning: 2025-08-26T09:49:04.956Z

Learnt from: pengbowang-nv
Repo: NVIDIA/TensorRT-LLM PR: 7192
File: tests/integration/test_lists/test-db/l0_dgx_b200.yml:56-72
Timestamp: 2025-08-26T09:49:04.956Z
Learning: In TensorRT-LLM test configuration files, the test scheduling system handles wildcard matching with special rules that prevent duplicate test execution even when the same tests appear in multiple yaml files with overlapping GPU wildcards (e.g., "*b200*" and "*gb200*").

Applied to files:

• docs/source/legacy/performance/perf-benchmarking.md
• docs/source/developer-guide/perf-benchmarking.md
• examples/models/core/deepseek_v3/README.md
• examples/models/core/llama4/README.md

📚 Learning: 2025-08-11T20:09:24.389Z

Learnt from: achartier
Repo: NVIDIA/TensorRT-LLM PR: 6763
File: tests/integration/defs/triton_server/conftest.py:16-22
Timestamp: 2025-08-11T20:09:24.389Z
Learning: In the TensorRT-LLM test infrastructure, the team prefers simple, direct solutions (like hard-coding directory traversal counts) over more complex but robust approaches when dealing with stable directory structures. They accept the maintenance cost of updating tests if the layout changes.

Applied to files:

• docs/source/legacy/performance/perf-benchmarking.md
• docs/source/developer-guide/perf-benchmarking.md
• examples/models/core/deepseek_v3/README.md
• examples/models/core/llama/README.md
• docs/source/features/quantization.md
• examples/models/core/llama4/README.md
• examples/models/core/exaone/README.md

📚 Learning: 2025-11-27T09:23:18.742Z

Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 9511
File: tests/integration/defs/examples/serve/test_serve.py:136-186
Timestamp: 2025-11-27T09:23:18.742Z
Learning: In TensorRT-LLM testing, when adding test cases based on RCCA commands, the command format should be copied exactly as it appears in the RCCA case, even if it differs from existing tests. For example, some RCCA commands for trtllm-serve may omit the "serve" subcommand while others include it.

Applied to files:

• docs/source/legacy/performance/perf-benchmarking.md
• docs/source/developer-guide/perf-benchmarking.md
• examples/models/core/llama/README.md
• examples/models/core/llama4/README.md
• examples/llm-api/_tensorrt_engine/quickstart_example.py

📚 Learning: 2025-08-26T09:37:10.463Z

Learnt from: jiaganc
Repo: NVIDIA/TensorRT-LLM PR: 7031
File: tensorrt_llm/bench/dataclasses/configuration.py:90-104
Timestamp: 2025-08-26T09:37:10.463Z
Learning: In TensorRT-LLM, the `get_pytorch_perf_config()` method returns `self.pytorch_config` which can contain default `cuda_graph_config` values, so `llm_args` may already have this config before the extra options processing.

Applied to files:

• docs/source/legacy/performance/perf-benchmarking.md
• docs/source/developer-guide/perf-benchmarking.md
• examples/models/core/deepseek_v3/README.md
• docs/source/features/quantization.md
• examples/llm-api/_tensorrt_engine/quickstart_example.py
• examples/llm-api/_tensorrt_engine/llm_medusa_decoding.py

📚 Learning: 2025-09-23T15:13:48.819Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/kernels/nccl_device/multimem.h:20-30
Timestamp: 2025-09-23T15:13:48.819Z
Learning: TRT-LLM targets modern CUDA toolkits that support FP8 datatypes, so cuda_fp8.h can be included unconditionally without version guards in TRT-LLM code.

Applied to files:

• docs/source/legacy/performance/perf-benchmarking.md
• docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md
• examples/models/core/deepseek_v3/README.md
• examples/models/core/llama/README.md
• docs/source/torch/features/quantization.md
• examples/models/core/llama4/README.md

📚 Learning: 2025-08-01T15:14:45.673Z

Learnt from: yibinl-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 6506
File: examples/models/core/mixtral/requirements.txt:3-3
Timestamp: 2025-08-01T15:14:45.673Z
Learning: In TensorRT-LLM, examples directory can have different dependency versions than the root requirements.txt file. Version conflicts between root and examples dependencies are acceptable because examples are designed to be standalone and self-contained.

Applied to files:

• docs/source/legacy/performance/perf-benchmarking.md
• docs/source/developer-guide/perf-benchmarking.md
• examples/quantization/README.md
• examples/models/core/deepseek_v3/README.md
• examples/models/core/llama/README.md
• examples/medusa/README.md
• docs/source/features/quantization.md
• examples/models/core/llama4/README.md
• examples/models/core/exaone/README.md

📚 Learning: 2025-08-18T08:42:02.640Z

Learnt from: samuellees
Repo: NVIDIA/TensorRT-LLM PR: 6974
File: tensorrt_llm/serve/scripts/benchmark_dataset.py:558-566
Timestamp: 2025-08-18T08:42:02.640Z
Learning: In TensorRT-LLM's RandomDataset (tensorrt_llm/serve/scripts/benchmark_dataset.py), when using --random-token-ids option, sequence length accuracy is prioritized over semantic correctness for benchmarking purposes. The encode/decode operations should use skip_special_tokens=True and add_special_tokens=False to ensure exact target token lengths.

Applied to files:

• examples/models/core/qwen/README.md

📚 Learning: 2025-08-20T07:43:36.447Z

Learnt from: ChristinaZ
Repo: NVIDIA/TensorRT-LLM PR: 7068
File: cpp/tensorrt_llm/kernels/moeTopKFuncs.cuh:169-172
Timestamp: 2025-08-20T07:43:36.447Z
Learning: In TensorRT-LLM MOE kernels, when processing up to 128 experts across 32 threads, each thread handles at most 4 experts (N < 5 constraint), where N represents candidates per thread rather than total system capacity.

Applied to files:

• examples/models/core/qwen/README.md
• docs/source/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md

📚 Learning: 2025-09-23T15:12:38.312Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device implementation, NCCL version 2.28+ requirements are handled at runtime in the nccl_device/config layer rather than with compile-time guards. This allows the allreduceOp to remain version-agnostic and delegates version compatibility validation to the appropriate lower-level components that can gracefully handle unsupported configurations.

Applied to files:

• docs/source/developer-guide/perf-benchmarking.md
• examples/quantization/README.md
• examples/models/core/deepseek_v3/README.md
• examples/models/core/llama/README.md
• ATTRIBUTIONS-Python.md
• docs/source/features/quantization.md
• examples/models/core/llama4/README.md

📚 Learning: 2025-08-21T02:39:12.009Z

Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 7104
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:1475-1480
Timestamp: 2025-08-21T02:39:12.009Z
Learning: The min latency mode functionality in TensorRT-LLM MOE kernels (cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu) is deprecated and no longer being maintained/updated, as confirmed by djns99. Bug reports and optimization suggestions for the computeStridesTmaWarpSpecializedLowLatencyKernel and related min latency code paths should be deprioritized.

Applied to files:

• docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md

📚 Learning: 2025-09-19T21:28:13.751Z

Learnt from: jhaotingc
Repo: NVIDIA/TensorRT-LLM PR: 7856
File: cpp/tensorrt_llm/thop/fp8BlockScaleMoe.cpp:159-166
Timestamp: 2025-09-19T21:28:13.751Z
Learning: In TensorRT-LLM blockScaleMoe routing (cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/runner.cu), the DeepSeek routing method performs reinterpret_cast<float*>(routingLogits) at line 89, which could cause issues if routing_logits are BF16. However, Qwen3-FP8 models use RenormalizeNaive routing method and are not affected by this dtype casting issue.

Applied to files:

• docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md
• docs/source/blogs/tech_blog/blog3_Optimizing_DeepSeek_R1_Throughput_on_NVIDIA_Blackwell_GPUs.md
• examples/models/core/exaone/README.md

📚 Learning: 2025-08-14T15:43:23.107Z

Learnt from: MatthiasKohl
Repo: NVIDIA/TensorRT-LLM PR: 6904
File: tensorrt_llm/_torch/attention_backend/trtllm.py:259-262
Timestamp: 2025-08-14T15:43:23.107Z
Learning: In TensorRT-LLM's attention backend, tensor parameters in the plan() method are assigned directly without validation (dtype, device, contiguity checks). This maintains consistency across all tensor inputs and follows the pattern of trusting callers to provide correctly formatted tensors.

Applied to files:

• docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md
• docs/source/features/quantization.md

📚 Learning: 2025-09-29T15:14:28.503Z

Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation.

Applied to files:

• docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md

📚 Learning: 2025-08-15T06:46:53.813Z

Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6767
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-15T06:46:53.813Z
Learning: In the TensorRT-LLM KV cache manager, SWA (Sliding Window Attention) combined with beam search is currently in a broken/non-functional state and is planned for future rework. During preparatory refactoring phases, code related to SWA+beam search may intentionally remain in a non-working state until the broader rework is completed.

Applied to files:

• docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md

📚 Learning: 2025-09-29T15:14:28.503Z

Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation with asserts for total size and TP divisibility.

Applied to files:

• docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md

📚 Learning: 2025-08-18T09:08:07.687Z

Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 6984
File: cpp/tensorrt_llm/CMakeLists.txt:297-299
Timestamp: 2025-08-18T09:08:07.687Z
Learning: In the TensorRT-LLM project, artifacts are manually copied rather than installed via `cmake --install`, so INSTALL_RPATH properties are not needed - only BUILD_RPATH affects the final artifacts.

Applied to files:

• examples/quantization/README.md

📚 Learning: 2025-10-17T13:21:31.724Z

Learnt from: ixlmar
Repo: NVIDIA/TensorRT-LLM PR: 8398
File: tensorrt_llm/_torch/pyexecutor/sampling_utils.py:237-272
Timestamp: 2025-10-17T13:21:31.724Z
Learning: The setup.py file in TensorRT-LLM explicitly requires Python 3.10+ via `python_requires=">=3.10, <4"`, making match/case statements and other Python 3.10+ features appropriate throughout the codebase.

Applied to files:

• examples/quantization/README.md

📚 Learning: 2025-08-27T14:23:55.566Z

Learnt from: ixlmar
Repo: NVIDIA/TensorRT-LLM PR: 7294
File: tensorrt_llm/_torch/modules/rms_norm.py:17-17
Timestamp: 2025-08-27T14:23:55.566Z
Learning: The TensorRT-LLM project requires Python 3.10+ as evidenced by the use of TypeAlias from typing module, match/case statements, and union type | syntax throughout the codebase, despite some documentation still mentioning Python 3.8+.

Applied to files:

• examples/quantization/README.md
• examples/models/core/llama/README.md
• docs/source/features/quantization.md
• examples/models/core/llama4/README.md
• examples/models/core/exaone/README.md
• README.md

📚 Learning: 2025-09-16T09:30:09.716Z

Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 7763
File: cpp/tensorrt_llm/CMakeLists.txt:297-301
Timestamp: 2025-09-16T09:30:09.716Z
Learning: In the TensorRT-LLM project, NCCL libraries are loaded earlier by PyTorch libraries or the bindings library, so the main shared library doesn't need NCCL paths in its RPATH - the libraries will already be available in the process address space when needed.

Applied to files:

• examples/quantization/README.md
• ATTRIBUTIONS-Python.md
• docs/source/features/quantization.md

📚 Learning: 2025-08-21T21:48:35.135Z

Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 7104
File: cpp/tensorrt_llm/cutlass_extensions/include/cutlass_extensions/epilogue/fusion/sm90_visitor_scatter.hpp:399-417
Timestamp: 2025-08-21T21:48:35.135Z
Learning: CUTLASS extensions in TensorRT-LLM (located under cpp/tensorrt_llm/cutlass_extensions/) are designed to integrate with and extend functionality in the external CUTLASS repository. When analyzing these extensions, their consumers and functionality wiring may exist in the CUTLASS codebase rather than within TensorRT-LLM itself.

Applied to files:

• examples/quantization/README.md

📚 Learning: 2025-08-27T17:50:13.264Z

Learnt from: venkywonka
Repo: NVIDIA/TensorRT-LLM PR: 6029
File: .github/pull_request_template.md:45-53
Timestamp: 2025-08-27T17:50:13.264Z
Learning: For PR templates in TensorRT-LLM, avoid suggesting changes that would increase developer overhead, such as converting plain bullets to mandatory checkboxes. The team prefers guidance-style bullets that don't require explicit interaction to reduce friction in the PR creation process.

Applied to files:

• examples/models/core/llama/README.md
• docs/source/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md
• examples/models/core/llama4/README.md

📚 Learning: 2025-10-22T06:53:47.017Z

Learnt from: xinhe-nv
Repo: NVIDIA/TensorRT-LLM PR: 8534
File: scripts/format_test_list.py:1-6
Timestamp: 2025-10-22T06:53:47.017Z
Learning: The file `scripts/format_test_list.py` in the TensorRT-LLM repository does not require the NVIDIA Apache-2.0 copyright header.

Applied to files:

• ATTRIBUTIONS-Python.md

📚 Learning: 2025-11-14T11:22:03.729Z

Learnt from: nzmora-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 9163
File: tensorrt_llm/_torch/auto_deploy/custom_ops/quant.py:107-113
Timestamp: 2025-11-14T11:22:03.729Z
Learning: In TensorRT-LLM AutoDeploy custom ops, when adding hardware capability checks to select between kernel implementations (e.g., cuBLAS vs. CUDA kernel), use descriptive variable names that identify the specific GPU architectures or families being targeted (e.g., `is_blackwell_geforce_or_ada`) rather than generic names like `enable_cuda_core`. This makes it clear that the code is selecting an implementation path based on hardware capabilities, not enabling/disabling hardware features.

Applied to files:

• docs/source/blogs/tech_blog/blog3_Optimizing_DeepSeek_R1_Throughput_on_NVIDIA_Blackwell_GPUs.md
• docs/source/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md

📚 Learning: 2025-09-18T05:41:45.847Z

Learnt from: pengbowang-nv
Repo: NVIDIA/TensorRT-LLM PR: 7120
File: tensorrt_llm/llmapi/llm.py:690-697
Timestamp: 2025-09-18T05:41:45.847Z
Learning: Kimi model support is currently focused on the PyTorch backend path, with TRT path support potentially coming later.

Applied to files:

• docs/source/torch/features/quantization.md
• docs/source/features/quantization.md
• examples/models/core/exaone/README.md

📚 Learning: 2025-08-19T12:45:11.997Z

Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 7033
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:0-0
Timestamp: 2025-08-19T12:45:11.997Z
Learning: In tensorrt_llm/_torch/pyexecutor/model_engine.py, DoRA (Delta Orthogonal Rank Adaptation) functionality was removed from the PyTorch flow to eliminate issues with inverted DoRA detection logic. The original is_dora condition was checking if scaling_vec_pointer == 0, which was potentially incorrect.

Applied to files:

• docs/source/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md
• examples/models/core/exaone/README.md
• examples/llm-api/_tensorrt_engine/quickstart_example.py
• examples/llm-api/_tensorrt_engine/llm_medusa_decoding.py

📚 Learning: 2025-08-14T23:23:27.449Z

Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6915
File: cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu:4010-4012
Timestamp: 2025-08-14T23:23:27.449Z
Learning: For MOE (Mixture of Experts) code reviews in TensorRT-LLM, avoid repeatedly suggesting finalize fusion validation checks and safety assertions. The user djns99 has indicated these suggestions are repetitive and unwanted across multiple MOE-related changes.

Applied to files:

• docs/source/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md

📚 Learning: 2025-07-17T09:01:27.402Z

Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 5616
File: tensorrt_llm/executor/worker.py:375-384
Timestamp: 2025-07-17T09:01:27.402Z
Learning: In tensorrt_llm/executor/worker.py, the LoRA adapter cache optimization logic that checks `is_adapter_in_cpu_cache()` and conditionally passes None for weights/config has a known race condition issue that cannot be solved with simple error handling or verification checks. This is a known limitation that requires a more comprehensive solution.

Applied to files:

• examples/models/core/exaone/README.md

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

🔇 Additional comments (24)

docs/source/legacy/performance/perf-benchmarking.md (1)

665-665: Revert the repository URL to the correct official location.

The documentation change updates the product name from "TensorRT Model Optimizer" to "Model Optimizer", but the repository URL change to NVIDIA/Model-Optimizer is incorrect. The official NVIDIA repository is NVIDIA/TensorRT-Model-Optimizer. Update the link to use the correct repository URL to ensure users access the actual project.

⛔ Skipped due to learnings

Learnt from: farshadghodsian
Repo: NVIDIA/TensorRT-LLM PR: 7101
File: docs/source/blogs/tech_blog/blog9_Deploying_GPT_OSS_on_TRTLLM.md:36-36
Timestamp: 2025-08-21T00:16:56.457Z
Learning: TensorRT-LLM container release tags in documentation should only reference published NGC container images. The README badge version may be ahead of the actual published container versions.

examples/llm-api/llm_inference.py (1)

10-10: Comment rebrand looks good

The comment now correctly references Model Optimizer’s quantized checkpoints; no functional changes and the guidance remains accurate.

examples/quantization/README.md (1)

14-14: Installation wording is clear and consistent

The new sentence accurately reflects that the NVIDIA Model Optimizer quantization toolkit is pulled in as a dependency; fits well with the rest of the README.

docs/source/blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.md (1)

49-51: Model Optimizer attribution and links look correct

The updated reference to the NVIDIA Model Optimizer and the added Hugging Face checkpoint links align with the rest of the blog and the overall rebranding in this PR.

examples/llm-api/_tensorrt_engine/quickstart_example.py (1)

12-12: Consistent branding update in example comment

The comment now matches the Model Optimizer terminology used elsewhere in the repo; no behavioral change.

docs/source/blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.md (1)

70-70: Updated toolkit attribution looks good

Referencing the “NVIDIA Model Optimizer toolkit” here is consistent with the rest of the documentation and correctly links to the Model-Optimizer repository.

examples/llm-api/quickstart_example.py (1)

7-7: Quickstart comment rebranding is correct

The quickstart example now consistently refers to Model Optimizer’s quantized checkpoints; no further changes needed.

examples/medusa/README.md (1)

22-22: LGTM! Documentation update aligns with rebranding.

The terminology update from "TensorRT Model Optimizer" to "Model Optimizer" is consistent with the PR's rebranding objective.

docs/source/developer-guide/perf-overview.md (1)

24-24: LGTM! Documentation link updated correctly.

The ModelOpt link has been updated to reflect the rebranding. The change is consistent with other updates in this PR.

examples/llm-api/_tensorrt_engine/llm_medusa_decoding.py (2)

32-32: LGTM! Comment updated for rebranding.

The comment correctly reflects the new "Model Optimizer" branding.

---

88-88: LGTM! Help text updated for rebranding.

The argparse help text has been correctly updated to reference "Model Optimizer" instead of "TensorRT Model Optimizer".

examples/models/core/llama/README.md (1)

1562-1562: LGTM! Documentation text updated for rebranding.

The descriptor has been correctly updated to reflect the new "Model Optimizer" branding in the OpenAI-compatible API server section.

examples/disaggregated/README.md (1)

215-215: LGTM! Repository link updated correctly.

The link has been updated to reference the rebranded Model Optimizer repository. This is consistent with the broader PR changes.

docs/source/developer-guide/perf-benchmarking.md (2)

426-426: LGTM! Documentation link updated correctly.

The ModelOpt documentation link has been updated to reference the rebranded repository path.

---

429-443: LGTM! Repository reference and example updated for rebranding.

The text reference and JSON example are consistent with the Model Optimizer rebranding. The example JSON correctly shows "modelopt" as the producer name.

examples/models/core/deepseek_v3/README.md (1)

776-776: Consistent re-branding from TensorRT-Model-Optimizer to Model-Optimizer.

The terminology and repository URLs have been consistently updated across the Deep Seek-V3 README. References to the optimizer tool now correctly point to NVIDIA/Model-Optimizer and use "Model Optimizer" in descriptive text.

Also applies to: 811-811, 818-818

docs/source/blogs/tech_blog/blog3_Optimizing_DeepSeek_R1_Throughput_on_NVIDIA_Blackwell_GPUs.md (1)

32-32: Consistent attribution update to Model Optimizer.

The reference to the Model Optimizer repository and naming has been correctly updated to reflect the re-branding.

README.md (1)

167-167: Consistent re-branding in news items.

News items referencing the Model Optimizer have been correctly updated to use the new project name consistently across both current and previous news sections.

Also applies to: 212-212

docs/source/features/quantization.md (1)

26-26: Consistent re-branding and URL updates in quantization documentation.

References to Model Optimizer have been consistently updated throughout the quantization guide, including repository URLs, support matrix links, and abbreviated terminology (ModelOpt).

Also applies to: 52-52, 57-57, 111-111

examples/auto_deploy/README.md (1)

93-93: Consistent re-branding in AutoDeploy documentation.

Section headings, external documentation links, and repository references have been consistently updated to Model Optimizer across the AutoDeploy README.

Also applies to: 95-95, 98-98, 102-102

examples/models/core/qwen/README.md (1)

666-668: Consistent repository path and terminology updates in Qwen README.

All references to the Model Optimizer repository have been updated from TensorRT-Model-Optimizer to Model-Optimizer in both clone commands and directory path references. Terminology is consistent with the new project naming.

Also applies to: 674-674, 690-690, 730-730

examples/models/core/exaone/README.md (1)

88-88: Consistent re-branding throughout EXAONE README.

References to Model Optimizer have been consistently updated across the EXAONE documentation, including repository paths, terminology, and external documentation links. The troubleshooting section correctly references the updated project name.

Also applies to: 93-93, 98-98, 110-110

ATTRIBUTIONS-Python.md (2)

25489-25489: Verify the new repository URL is correct and accessible.

The URL has been updated to reflect the rebranding. Please confirm that https://github.com/NVIDIA/Model-Optimizer is the correct canonical repository for the Model Optimizer project and that it is publicly accessible.

---

25516-25516: Verify consistency of attribution updates across the file.

This appears to be another instance of the rebranding URL change. Ensure that all references to the old repository path have been updated consistently throughout the attribution file, and that no stale references remain.

[QiJune]

LGTM

[cjluo-nv]

/bot run

[tensorrt-cicd]

PR_Github #27158 [ run ] triggered by Bot. Commit: 6d1ad01

[tensorrt-cicd]

PR_Github #27158 [ run ] completed with state SUCCESS. Commit: 6d1ad01
/LLM/main/L0_MergeRequest_PR pipeline #20722 completed with status: 'FAILURE'

[nv-guomingz]

/bot run

[tensorrt-cicd]

PR_Github #27196 [ run ] triggered by Bot. Commit: 2eddd18

[kevalmorabia97]

Model Optimizer Repository renamed. Feel free to merge this PR now

[tensorrt-cicd]

PR_Github #27196 [ run ] completed with state SUCCESS. Commit: 2eddd18
/LLM/main/L0_MergeRequest_PR pipeline #20758 completed with status: 'SUCCESS'
Pipeline passed with automatic retried tests. Check the rerun report for details.