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Comparison

JetStream vs TensorRT-LLM

Verdict

Pick JetStream if jetStream optimises throughput and memory for LLM inference on XLA devices like TPUs, with potential GPU support in future; 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 inference execution.

Markdown twin · JetStream alternatives · TensorRT-LLM alternatives

GraphCanon updated Sep 20, 2026

12views this month

JetStream logo

JetStream

AI-Hypercomputer/JetStream

★ 458pushed Jan 5, 2026
vs
TensorRT-LLM logo

TensorRT-LLM

NVIDIA/TensorRT-LLM

★ 15kpushed Sep 18, 2026

Trust & integrity

SignalJetStreamTensorRT-LLM
Maintenance
Slowing (257d since push)
As of Sep 20, 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 20, 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
JetStream trust reportTensorRT-LLM trust reportTrust methodology

Tagline

JetStream
Throughput and memory optimized engine for LLM inference on XLA devices
TensorRT-LLM
Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs

Stars

JetStream
458
TensorRT-LLM
15k

Forks

JetStream
67
TensorRT-LLM
2.8k

Open issues

JetStream
26
TensorRT-LLM
1.5k

Language

JetStream
Python
TensorRT-LLM
Python

Adopt for

JetStream
JetStream optimises throughput and memory for LLM inference on XLA devices like TPUs, with potential GPU support in future.
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

JetStream
-
TensorRT-LLM
-

Runtime

JetStream
-
TensorRT-LLM
-

License

JetStream
Apache-2.0
TensorRT-LLM
Other

Last pushed

JetStream
Jan 5, 2026
TensorRT-LLM
Sep 18, 2026

Categories

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

Trust and health

Maintenance

JetStream
Slowing (36%)
TensorRT-LLM
Very active (96%)

Days since push

JetStream
257d
TensorRT-LLM
0d

Open issues (now)

JetStream
26
TensorRT-LLM
1.5k

Stars delta

JetStream
+7 (30d)
TensorRT-LLM
+328 (30d)

Open issues delta

JetStream
+1 (30d)
TensorRT-LLM
-106 (30d)

OSV dependency advisories

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

Full report

JetStream
Trust report
TensorRT-LLM
Trust report

Choose JetStream if…

  • License: JetStream is Apache-2.0, TensorRT-LLM is Other.
  • Tags unique to JetStream: gemma, gpt, gpu, inference.
  • * You are working with large language models (LLMs) that require efficient inference on hardware supported by XLA, particularly TPUs.

When NOT to use JetStream

  • * If your primary compute platform is not an XLA-compatible device such as TPU; JetStream's current focus is on systems that are supported by XLA.
  • * When you need immediate support for GPUs, since GPU functionality is marked as a future potential enhancement.
GitHubDetails

Choose TensorRT-LLM if…

  • License: TensorRT-LLM is Other, JetStream is Apache-2.0.
  • Tags unique to TensorRT-LLM: blackwell, cuda, llm-serving, moe.
  • Also covers Model Training.
  • 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

JetStream 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: JetStream 458 · TensorRT-LLM 15k (synced Sep 20, 2026).

Common questions

What is the difference between JetStream and TensorRT-LLM?
JetStream: Throughput and memory optimized engine for LLM inference on XLA devices. 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 JetStream over TensorRT-LLM?
Choose JetStream over TensorRT-LLM when License: JetStream is Apache-2.0, TensorRT-LLM is Other; Tags unique to JetStream: gemma, gpt, gpu, inference; * You are working with large language models (LLMs) that require efficient inference on hardware supported by XLA, particularly TPUs.
When should I choose TensorRT-LLM over JetStream?
Choose TensorRT-LLM over JetStream when License: TensorRT-LLM is Other, JetStream is Apache-2.0; Tags unique to TensorRT-LLM: blackwell, cuda, llm-serving, moe; Also covers Model Training; When you need to optimize Large Language Models for efficient inference on NVIDIA GPUs.
When should I avoid JetStream?
* If your primary compute platform is not an XLA-compatible device such as TPU; JetStream's current focus is on systems that are supported by XLA. * When you need immediate support for GPUs, since GPU functionality is marked as a future potential enhancement.
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 JetStream or TensorRT-LLM more popular on GitHub?
TensorRT-LLM has more GitHub stars (14,645 vs 458). Stars measure visibility, not whether either tool fits your constraints.
Are JetStream and TensorRT-LLM open source?
Yes - both are open-source projects on GitHub (JetStream: Apache-2.0, TensorRT-LLM: Other).
Where can I find alternatives to JetStream or TensorRT-LLM?
GraphCanon lists graph-backed alternatives at JetStream alternatives and TensorRT-LLM alternatives (JetStream 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, JetStream or TensorRT-LLM?
JetStream: Slowing. 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 JetStream and TensorRT-LLM?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: JetStream trust report; TensorRT-LLM trust report.

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