Comparison
JetStream vs airllm
Verdict
Pick JetStream if jetStream optimises throughput and memory for LLM inference on XLA devices like TPUs, with potential GPU support in future; pick airllm if airLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.
Markdown twin · JetStream alternatives · airllm alternatives
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Trust & integrity
| Signal | JetStream | airllm |
|---|---|---|
| Maintenance | Slowing (231d since push) As of today · github_public_v1 | Very active (5d since push) As of 4w · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of 4w · 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
- JetStream
- Throughput and memory optimized engine for LLM inference on XLA devices
- airllm
- AirLLM 70B inference with single 4GB GPU
Stars
- JetStream
- 455
- airllm
- 24k
Forks
- JetStream
- 67
- airllm
- 2.7k
Open issues
- JetStream
- 26
- airllm
- 115
Language
- JetStream
- Python
- airllm
- Jupyter Notebook
Adopt for
- JetStream
- JetStream optimises throughput and memory for LLM inference on XLA devices like TPUs, with potential GPU support in future.
- airllm
- AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.
Persona
- JetStream
- -
- airllm
- -
Runtime
- JetStream
- -
- airllm
- -
License
- JetStream
- Apache-2.0
- airllm
- Apache-2.0
Last pushed
- JetStream
- Jan 5, 2026
- airllm
- Jul 23, 2026
Categories
- JetStream
- Inference & Serving
- airllm
- Inference & Serving
Trust and health
Maintenance
- JetStream
- Slowing (36%)
- airllm
- Very active (96%)
Days since push
- JetStream
- 231d
- airllm
- 5d
Open issues (now)
- JetStream
- 26
- airllm
- 115
Stars delta
- JetStream
- +4 (30d)
- airllm
- Unknown
Open issues delta
- JetStream
- +1 (30d)
- airllm
- Unknown
Owner type
- JetStream
- Organization
- airllm
- User
OSV dependency advisories
- JetStream
- No lockfile (source not queried)
- airllm
- Published findings
Full report
- JetStream
- Trust report
- airllm
- Trust report
Shared compatibility
- Python · JetStream: Python runtime · airllm: Python runtime
Choose JetStream if…
- JetStream is primarily Python; airllm is Jupyter Notebook.
- 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.
Choose airllm if…
- airllm is primarily Jupyter Notebook; JetStream is Python.
- Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply..
- Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences..
- Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai.
- If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
When NOT to use airllm
- Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency.
- Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
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 Aug 25, 2026
- GitHub forks (AI-Hypercomputer/JetStream) · observed Aug 25, 2026
- Last push (AI-Hypercomputer/JetStream) · observed Jan 5, 2026
- License file (Apache-2.0) · observed Aug 25, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (lyogavin/airllm) · observed Jul 28, 2026
- GitHub forks (lyogavin/airllm) · observed Jul 28, 2026
- Last push (lyogavin/airllm) · observed Jul 23, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 9, 2026
GitHub stars on cards: JetStream 455 · airllm 24k (synced Aug 25, 2026).
Common questions
- What is the difference between JetStream and airllm?
- JetStream: Throughput and memory optimized engine for LLM inference on XLA devices. airllm: AirLLM 70B inference with single 4GB GPU. See the comparison table for live GitHub stats and shared categories.
- When should I choose JetStream over airllm?
- Choose JetStream over airllm when JetStream is primarily Python; airllm is Jupyter Notebook; 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 airllm over JetStream?
- Choose airllm over JetStream when airllm is primarily Jupyter Notebook; JetStream is Python; Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply.; Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences.; Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai; If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
- 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 airllm?
- Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency. Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
- Is JetStream or airllm more popular on GitHub?
- airllm has more GitHub stars (24,183 vs 455). Stars measure visibility, not whether either tool fits your constraints.
- Are JetStream and airllm open source?
- Yes - both are open-source projects on GitHub (JetStream: Apache-2.0, airllm: Apache-2.0).
- Where can I find alternatives to JetStream or airllm?
- GraphCanon lists graph-backed alternatives at JetStream alternatives and airllm alternatives (JetStream markdown twin, airllm 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 airllm?
- JetStream: Slowing. airllm: 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 airllm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: JetStream trust report; airllm trust report.