--- title: "JetStream vs airllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ai-hypercomputer-jetstream-vs-lyogavin-airllm" tools: ["ai-hypercomputer-jetstream", "lyogavin-airllm"] --- # JetStream vs airllm *GraphCanon updated Aug 25, 2026* ## 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. [JetStream](https://github.com/AI-Hypercomputer/JetStream) reports 455 GitHub stars, 67 forks, and 26 open issues, last pushed Jan 5, 2026. [airllm](https://github.com/lyogavin/airllm) has 24k stars, 2.7k forks, and 115 open issues, last pushed Jul 23, 2026. Figures are from public GitHub metadata via [JetStream's repository](https://github.com/AI-Hypercomputer/JetStream) and [airllm's repository](https://github.com/lyogavin/airllm). | | [JetStream](/tools/ai-hypercomputer-jetstream.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Tagline | Throughput and memory optimized engine for LLM inference on XLA devices | AirLLM 70B inference with single 4GB GPU | | Stars | 455 | 24,183 | | Forks | 67 | 2,722 | | Open issues | 26 | 115 | | Language | Python | Jupyter Notebook | | Adopt for | JetStream optimises throughput and memory for LLM inference on XLA devices like TPUs, with potential GPU support in future. | AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [JetStream](/tools/ai-hypercomputer-jetstream.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 231d | 5d | | Open issues (now) | 26 | 115 | | Stars delta | +4 (30d) | Unknown | | Open issues delta | +1 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/ai-hypercomputer-jetstream/trust.md) | [trust report](/tools/lyogavin-airllm/trust.md) | ## Shared compatibility - **Python**: [JetStream](/tools/ai-hypercomputer-jetstream.md) - Python runtime; [airllm](/tools/lyogavin-airllm.md) - Python runtime ## Decision facts: JetStream - **Adopt for:** JetStream optimises throughput and memory for LLM inference on XLA devices like TPUs, with potential GPU support in future. ## Decision facts: airllm - **Pricing:** freemium - 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. - **Adopt for:** AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU. - **License detail:** Apache-2.0 ## Choose when ### 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. ### 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 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 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. ## 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](/tools/ai-hypercomputer-jetstream/alternatives) and [airllm alternatives](/tools/lyogavin-airllm/alternatives) ([JetStream markdown twin](/tools/ai-hypercomputer-jetstream/alternatives.md), [airllm markdown twin](/tools/lyogavin-airllm/alternatives.md)), 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](/compare/ai-hypercomputer-jetstream-vs-lyogavin-airllm.md) 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](/tools/ai-hypercomputer-jetstream/trust); [airllm trust report](/tools/lyogavin-airllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ai-hypercomputer-jetstream`](/api/graphcanon/graph?tool=ai-hypercomputer-jetstream) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_