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Comparison

DeepSpeed vs TensorRT-LLM

Verdict

Pick DeepSpeed if decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression; pick TensorRT-LLM if tensorRT-LLM is a Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs, offering components for creating Python and C++ runtimes for performant.

Markdown twin · DeepSpeed alternatives · TensorRT-LLM alternatives

GraphCanon updated Sep 20, 2026

10views this month

DeepSpeed logo

DeepSpeed

deepspeedai/DeepSpeed

★ 43kpushed Sep 6, 2026
vs
TensorRT-LLM logo

TensorRT-LLM

NVIDIA/TensorRT-LLM

★ 15kpushed Sep 18, 2026

Trust & integrity

SignalDeepSpeedTensorRT-LLM
Maintenance
Very active (0d since push)
As of Sep 6, 2026 · github_public_v1
Very active (0d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 6, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
Published findings
As of Sep 18, 2026 · 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
DeepSpeed trust reportTensorRT-LLM trust reportTrust methodology

Tagline

DeepSpeed
Deep learning optimization library for efficient distributed training and inference
TensorRT-LLM
Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs

Stars

DeepSpeed
43k
TensorRT-LLM
15k

Forks

DeepSpeed
5.0k
TensorRT-LLM
2.8k

Open issues

DeepSpeed
1.4k
TensorRT-LLM
1.5k

Language

DeepSpeed
Python
TensorRT-LLM
Python

Adopt for

DeepSpeed
Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression.
TensorRT-LLM
TensorRT-LLM is a Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs, offering components for creating Python and C++ runtimes for performant inference execution.

Persona

DeepSpeed
-
TensorRT-LLM
-

Runtime

DeepSpeed
-
TensorRT-LLM
-

License

DeepSpeed
Apache-2.0
TensorRT-LLM
Other

Last pushed

DeepSpeed
Sep 6, 2026
TensorRT-LLM
Sep 18, 2026

Categories

DeepSpeed
Inference & Serving, Model Training
TensorRT-LLM
Inference & Serving, Model Training

Trust and health

Open issues (now)

DeepSpeed
1.4k
TensorRT-LLM
1.5k

Stars delta

DeepSpeed
+195 (30d)
TensorRT-LLM
+328 (30d)

Open issues delta

DeepSpeed
+77 (30d)
TensorRT-LLM
-106 (30d)

OSV dependency advisories

DeepSpeed
No lockfile (source not queried)
TensorRT-LLM
Published findings

Full report

DeepSpeed
Trust report
TensorRT-LLM
Trust report

Choose DeepSpeed if…

  • License: DeepSpeed is Apache-2.0, TensorRT-LLM is Other.
  • Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning.
  • - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters)

When NOT to use DeepSpeed

  • - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs
  • - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
GitHubDetails

Choose TensorRT-LLM if…

  • License: TensorRT-LLM is Other, DeepSpeed is Apache-2.0.
  • Tags unique to TensorRT-LLM: blackwell, cuda, llm-serving, moe.
  • When you need to optimize Large Language Models for efficient inference on NVIDIA GPUs.

When NOT to use TensorRT-LLM

  • If your hardware does not include NVIDIA GPUs, as TensorRT-LLM is specifically optimized for NVIDIA GPUs.
  • If you do not require a Python API for defining and optimizing LLMs, and prefer a different interface or tool.
  • When you are working in a non-NVIDIA GPU environment, as TensorRT-LLM's optimizations are tailored to NVIDIA hardware.
GitHubDetails

Explore

DeepSpeed trust report →TensorRT-LLM trust report →Inference & Serving category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: DeepSpeed 43k · TensorRT-LLM 15k (synced Sep 20, 2026).

Common questions

What is the difference between DeepSpeed and TensorRT-LLM?
DeepSpeed: Deep learning optimization library for efficient distributed training and inference. TensorRT-LLM: Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs. See the comparison table for live GitHub stats and shared categories.
When should I choose DeepSpeed over TensorRT-LLM?
Choose DeepSpeed over TensorRT-LLM when License: DeepSpeed is Apache-2.0, TensorRT-LLM is Other; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters).
When should I choose TensorRT-LLM over DeepSpeed?
Choose TensorRT-LLM over DeepSpeed when License: TensorRT-LLM is Other, DeepSpeed is Apache-2.0; Tags unique to TensorRT-LLM: blackwell, cuda, llm-serving, moe; When you need to optimize Large Language Models for efficient inference on NVIDIA GPUs.
When should I avoid DeepSpeed?
- When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
When should I avoid TensorRT-LLM?
If your hardware does not include NVIDIA GPUs, as TensorRT-LLM is specifically optimized for NVIDIA GPUs. If you do not require a Python API for defining and optimizing LLMs, and prefer a different interface or tool. When you are working in a non-NVIDIA GPU environment, as TensorRT-LLM's optimizations are tailored to NVIDIA hardware.
Is DeepSpeed or TensorRT-LLM more popular on GitHub?
DeepSpeed has more GitHub stars (43,065 vs 14,645). Stars measure visibility, not whether either tool fits your constraints.
Are DeepSpeed and TensorRT-LLM open source?
Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, TensorRT-LLM: Other).
Where can I find alternatives to DeepSpeed or TensorRT-LLM?
GraphCanon lists graph-backed alternatives at DeepSpeed alternatives and TensorRT-LLM alternatives (DeepSpeed 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, DeepSpeed or TensorRT-LLM?
DeepSpeed: Very 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 DeepSpeed and TensorRT-LLM?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSpeed trust report; TensorRT-LLM trust report.

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