Comparison
JetStream vs TransformerEngine
Verdict
Pick JetStream if jetStream optimises throughput and memory for LLM inference on XLA devices like TPUs, with potential GPU support in future; pick TransformerEngine if transformerEngine optimizes Transformer model performance with FP8/FP4 precision on NVIDIA GPUs like Hopper, Ada, and Blackwell, boosting throughput and reducing memory usage.
Markdown twin · JetStream alternatives · TransformerEngine alternatives
GraphCanon updated Sep 20, 2026
13views this month
vs
Trust & integrity
| Signal | JetStream | TransformerEngine |
|---|---|---|
| Maintenance | Slowing (257d since push) As of Sep 20, 2026 · github_public_v1 | Very active (1d since push) As of Sep 7, 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 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 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 |
Tagline
- JetStream
- Throughput and memory optimized engine for LLM inference on XLA devices
- TransformerEngine
- A library for accelerating Transformer models on NVIDIA GPUs using low precision formats like FP8 and FP4.
Stars
- JetStream
- 458
- TransformerEngine
- 3.5k
Forks
- JetStream
- 67
- TransformerEngine
- 819
Open issues
- JetStream
- 26
- TransformerEngine
- 343
Language
- JetStream
- Python
- TransformerEngine
- Python
Adopt for
- JetStream
- JetStream optimises throughput and memory for LLM inference on XLA devices like TPUs, with potential GPU support in future.
- TransformerEngine
- TransformerEngine optimizes Transformer model performance with FP8/FP4 precision on NVIDIA GPUs like Hopper, Ada, and Blackwell, boosting throughput and reducing memory usage.
Persona
- JetStream
- -
- TransformerEngine
- -
Runtime
- JetStream
- -
- TransformerEngine
- -
License
- JetStream
- Apache-2.0
- TransformerEngine
- Apache-2.0
Last pushed
- JetStream
- Jan 5, 2026
- TransformerEngine
- Sep 5, 2026
Categories
- JetStream
- Inference & Serving
- TransformerEngine
- Inference & Serving, Model Training
Trust and health
Maintenance
- JetStream
- Slowing (36%)
- TransformerEngine
- Very active (96%)
Days since push
- JetStream
- 257d
- TransformerEngine
- 1d
Open issues (now)
- JetStream
- 26
- TransformerEngine
- 343
Stars delta
- JetStream
- +7 (30d)
- TransformerEngine
- +45 (30d)
Open issues delta
- JetStream
- +1 (30d)
- TransformerEngine
- +33 (30d)
Full report
- JetStream
- Trust report
- TransformerEngine
- Trust report
Choose JetStream if…
- Tags unique to JetStream: gemma, gpt, inference, large-language-models.
- * You are working with large language models (LLMs) that require efficient inference on hardware supported by XLA, particularly TPUs.
- Leaner open-issue backlog (26).
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.
Choose TransformerEngine if…
- Tags unique to TransformerEngine: cuda, deep-learning, fp4, fp8.
- Also covers Model Training.
- If you need high-throughput training or inference of Transformer models specifically on compatible NVIDIA GPUs (Hopper, Ada, Blackwell).
When NOT to use TransformerEngine
- Avoid if your project is not running on NVIDIA's Hopper, Ada, or Blackwell GPUs.
- If memory usage isn't a critical concern and you prefer higher precision over speed optimization.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AI-Hypercomputer/JetStream) · observed Sep 20, 2026
- GitHub forks (AI-Hypercomputer/JetStream) · observed Sep 20, 2026
- Last push (AI-Hypercomputer/JetStream) · observed Jan 5, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (NVIDIA/TransformerEngine) · observed Sep 20, 2026
- GitHub forks (NVIDIA/TransformerEngine) · observed Sep 20, 2026
- Last push (NVIDIA/TransformerEngine) · observed Sep 5, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: JetStream 458 · TransformerEngine 3.5k (synced Sep 20, 2026).
Common questions
- What is the difference between JetStream and TransformerEngine?
- JetStream: Throughput and memory optimized engine for LLM inference on XLA devices. TransformerEngine: A library for accelerating Transformer models on NVIDIA GPUs using low precision formats like FP8 and FP4.. See the comparison table for live GitHub stats and shared categories.
- When should I choose JetStream over TransformerEngine?
- Choose JetStream over TransformerEngine when Tags unique to JetStream: gemma, gpt, inference, large-language-models; * You are working with large language models (LLMs) that require efficient inference on hardware supported by XLA, particularly TPUs; Leaner open-issue backlog (26).
- When should I choose TransformerEngine over JetStream?
- Choose TransformerEngine over JetStream when Tags unique to TransformerEngine: cuda, deep-learning, fp4, fp8; Also covers Model Training; If you need high-throughput training or inference of Transformer models specifically on compatible NVIDIA GPUs (Hopper, Ada, Blackwell).
- 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 TransformerEngine?
- Avoid if your project is not running on NVIDIA's Hopper, Ada, or Blackwell GPUs. If memory usage isn't a critical concern and you prefer higher precision over speed optimization.
- Is JetStream or TransformerEngine more popular on GitHub?
- TransformerEngine has more GitHub stars (3,524 vs 458). Stars measure visibility, not whether either tool fits your constraints.
- Are JetStream and TransformerEngine open source?
- Yes - both are open-source projects on GitHub (JetStream: Apache-2.0, TransformerEngine: Apache-2.0).
- Where can I find alternatives to JetStream or TransformerEngine?
- GraphCanon lists graph-backed alternatives at JetStream alternatives and TransformerEngine alternatives (JetStream markdown twin, TransformerEngine 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 TransformerEngine?
- JetStream: Slowing. TransformerEngine: 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 TransformerEngine?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: JetStream trust report; TransformerEngine trust report.