Comparison
mlc-llm vs TensorRT-LLM
Verdict
Pick mlc-llm if mature deployment engine for efficient large-scale model serving, leveraging advanced compilation techniques; pick TensorRT-LLM if `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations.
Markdown twin · mlc-llm alternatives · TensorRT-LLM alternatives
GraphCanon updated 1w · 27 views this month
vs
Trust & integrity
| Signal | mlc-llm | TensorRT-LLM |
|---|---|---|
| Maintenance | Active (16d since push) As of 1w · github_public_v1 | Very active (0d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- mlc-llm
- Universal LLM Deployment Engine with ML Compilation
- TensorRT-LLM
- Python API for defining and optimizing Large Language Models (LLMs) on NVIDIA GPUs
Stars
- mlc-llm
- 23k
- TensorRT-LLM
- 14k
Forks
- mlc-llm
- 2.1k
- TensorRT-LLM
- 2.6k
Open issues
- mlc-llm
- 334
- TensorRT-LLM
- 1.6k
Language
- mlc-llm
- Python
- TensorRT-LLM
- Python
Adopt for
- mlc-llm
- Mature deployment engine for efficient large-scale model serving, leveraging advanced compilation techniques.
- TensorRT-LLM
- `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations.
Persona
- mlc-llm
- -
- TensorRT-LLM
- -
Runtime
- mlc-llm
- -
- TensorRT-LLM
- -
License
- mlc-llm
- Open-source under the Apache-2.0 license, allowing for free use in both open source and commercial contexts while requiring acknowledgment of its use.
- TensorRT-LLM
- Other
Last pushed
- mlc-llm
- Jul 31, 2026
- TensorRT-LLM
- Aug 7, 2026
Categories
- mlc-llm
- Inference & Serving, LLM Frameworks
- TensorRT-LLM
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- mlc-llm
- Active (82%)
- TensorRT-LLM
- Very active (96%)
Days since push
- mlc-llm
- 16d
- TensorRT-LLM
- 0d
Open issues (now)
- mlc-llm
- 334
- TensorRT-LLM
- 1.6k
Stars delta
- mlc-llm
- +103 (30d)
- TensorRT-LLM
- Unknown
Open issues delta
- mlc-llm
- +11 (30d)
- TensorRT-LLM
- Unknown
OSV dependency advisories
- mlc-llm
- No lockfile (source not queried)
- TensorRT-LLM
- Published findings
Full report
- mlc-llm
- Trust report
- TensorRT-LLM
- Trust report
Choose mlc-llm if…
- License: mlc-llm is Apache-2.0, TensorRT-LLM is Other.
- Requirements: - Requires familiarity with Python and machine learning concepts.; - Efficient with large language models but may have higher initial setup complexity due to specialized features..
- Tags unique to mlc-llm: language-model, llm, machine-learning-compilation, tvm.
- - When you need an efficient tool specifically designed with advanced compilation techniques that optimize performance for large language models (LLMs).
When NOT to use mlc-llm
- - Avoid mlc-llm if you are looking for a broader suite of tools; this tool focuses intensely on deployment efficiency via ML compilation techniques.
- - If you prefer tools with extensive third-party integrations or community-developed extensions, as mlc-llm's focus is narrow to deep optimization.
Choose TensorRT-LLM if…
- License: TensorRT-LLM is Other, mlc-llm is Apache-2.0.
- Pricing: Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions..
- Requirements: NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities..
- Tags unique to TensorRT-LLM: blackwell, cuda, llm-serving, moe.
- When you are developing or deploying large language models (LLMs) specifically on NVIDIA GPU hardware.
When NOT to use TensorRT-LLM
- When working on CPUs or non-NVIDIA GPUs as the optimizations and hardware support are NVIDIA-specific.
- If you prioritize portability across different frameworks over high-performance tuning since TensorRT LLM is tightly integrated with NVIDIA technologies.
- For projects that do not require deep level performance optimizations and prefer more general-purpose serving solutions.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (mlc-ai/mlc-llm) · observed Aug 17, 2026
- GitHub forks (mlc-ai/mlc-llm) · observed Aug 17, 2026
- Last push (mlc-ai/mlc-llm) · observed Jul 31, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (NVIDIA/TensorRT-LLM) · observed Aug 7, 2026
- GitHub forks (NVIDIA/TensorRT-LLM) · observed Aug 7, 2026
- Last push (NVIDIA/TensorRT-LLM) · observed Aug 7, 2026
- License file (Other) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: mlc-llm 23k · TensorRT-LLM 14k (synced Aug 17, 2026).
Common questions
- What is the difference between mlc-llm and TensorRT-LLM?
- mlc-llm: Universal LLM Deployment Engine with ML Compilation. TensorRT-LLM: Python API for defining and optimizing Large Language Models (LLMs) on NVIDIA GPUs. See the comparison table for live GitHub stats and shared categories.
- When should I choose mlc-llm over TensorRT-LLM?
- Choose mlc-llm over TensorRT-LLM when License: mlc-llm is Apache-2.0, TensorRT-LLM is Other; Requirements: - Requires familiarity with Python and machine learning concepts.; - Efficient with large language models but may have higher initial setup complexity due to specialized features.; Tags unique to mlc-llm: language-model, llm, machine-learning-compilation, tvm; - When you need an efficient tool specifically designed with advanced compilation techniques that optimize performance for large language models (LLMs).
- When should I choose TensorRT-LLM over mlc-llm?
- Choose TensorRT-LLM over mlc-llm when License: TensorRT-LLM is Other, mlc-llm is Apache-2.0; Pricing: Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions.; Requirements: NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities.; Tags unique to TensorRT-LLM: blackwell, cuda, llm-serving, moe; When you are developing or deploying large language models (LLMs) specifically on NVIDIA GPU hardware.
- When should I avoid mlc-llm?
- - Avoid mlc-llm if you are looking for a broader suite of tools; this tool focuses intensely on deployment efficiency via ML compilation techniques. - If you prefer tools with extensive third-party integrations or community-developed extensions, as mlc-llm's focus is narrow to deep optimization.
- When should I avoid TensorRT-LLM?
- When working on CPUs or non-NVIDIA GPUs as the optimizations and hardware support are NVIDIA-specific. If you prioritize portability across different frameworks over high-performance tuning since TensorRT LLM is tightly integrated with NVIDIA technologies. For projects that do not require deep level performance optimizations and prefer more general-purpose serving solutions.
- Is mlc-llm or TensorRT-LLM more popular on GitHub?
- mlc-llm has more GitHub stars (23,063 vs 14,317). Stars measure visibility, not whether either tool fits your constraints.
- Are mlc-llm and TensorRT-LLM open source?
- Yes - both are open-source projects on GitHub (mlc-llm: Apache-2.0, TensorRT-LLM: Other).
- Where can I find alternatives to mlc-llm or TensorRT-LLM?
- GraphCanon lists graph-backed alternatives at mlc-llm alternatives and TensorRT-LLM alternatives (mlc-llm markdown twin, TensorRT-LLM markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, mlc-llm or TensorRT-LLM?
- mlc-llm: Active. TensorRT-LLM: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for mlc-llm and TensorRT-LLM?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mlc-llm trust report; TensorRT-LLM trust report.