--- title: "JetStream vs TensorRT-LLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ai-hypercomputer-jetstream-vs-nvidia-tensorrt-llm" tools: ["ai-hypercomputer-jetstream", "nvidia-tensorrt-llm"] --- # JetStream vs TensorRT-LLM *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 TensorRT-LLM if `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations. [JetStream](https://github.com/AI-Hypercomputer/JetStream) reports 455 GitHub stars, 67 forks, and 26 open issues, last pushed Jan 5, 2026. [TensorRT-LLM](https://nvidia.github.io/TensorRT-LLM) has 14k stars, 2.6k forks, and 1.6k open issues, last pushed Aug 7, 2026. Figures are from public GitHub metadata via [JetStream's repository](https://github.com/AI-Hypercomputer/JetStream) and [TensorRT-LLM's repository](https://github.com/NVIDIA/TensorRT-LLM). | | [JetStream](/tools/ai-hypercomputer-jetstream.md) | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | | --- | --- | --- | | Tagline | Throughput and memory optimized engine for LLM inference on XLA devices | Python API for defining and optimizing Large Language Models (LLMs) on NVIDIA GPUs | | Stars | 455 | 14,317 | | Forks | 67 | 2,641 | | Open issues | 26 | 1,616 | | Language | Python | Python | | Adopt for | JetStream optimises throughput and memory for LLM inference on XLA devices like TPUs, with potential GPU support in future. | `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Inference & Serving | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [JetStream](/tools/ai-hypercomputer-jetstream.md) | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 231d | 0d | | Open issues (now) | 26 | 1.6k | | Stars delta | +4 (30d) | Unknown | | Open issues delta | +1 (30d) | Unknown | | Full report | [trust report](/tools/ai-hypercomputer-jetstream/trust.md) | [trust report](/tools/nvidia-tensorrt-llm/trust.md) | ## 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: TensorRT-LLM - **Pricing:** oss - Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions. - **Requirements:** NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities. - **Adopt for:** `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations. ## Choose when ### 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. ### Choose TensorRT-LLM if… - License: TensorRT-LLM is Other, JetStream is Apache-2.0. - Pricing: Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions.. - Requirements: NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities.. - Tags unique to TensorRT-LLM: blackwell, cuda, llm-serving, moe. - Also covers LLM Frameworks. - When you are developing or deploying large language models (LLMs) specifically on NVIDIA GPU hardware. ## 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 TensorRT-LLM - When working on CPUs or non-NVIDIA GPUs as the optimizations and hardware support are NVIDIA-specific. - If you prioritize portability across different frameworks over high-performance tuning since TensorRT LLM is tightly integrated with NVIDIA technologies. - For projects that do not require deep level performance optimizations and prefer more general-purpose serving solutions. ## 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 (LLMs) 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; Pricing: Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions.; Requirements: NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities.; Tags unique to TensorRT-LLM: blackwell, cuda, llm-serving, moe; Also covers LLM Frameworks; When you are developing or deploying large language models (LLMs) specifically on NVIDIA GPU hardware. ### 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? When working on CPUs or non-NVIDIA GPUs as the optimizations and hardware support are NVIDIA-specific. If you prioritize portability across different frameworks over high-performance tuning since TensorRT LLM is tightly integrated with NVIDIA technologies. For projects that do not require deep level performance optimizations and prefer more general-purpose serving solutions. ### Is JetStream or TensorRT-LLM more popular on GitHub? TensorRT-LLM has more GitHub stars (14,317 vs 455). 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](/tools/ai-hypercomputer-jetstream/alternatives) and [TensorRT-LLM alternatives](/tools/nvidia-tensorrt-llm/alternatives) ([JetStream markdown twin](/tools/ai-hypercomputer-jetstream/alternatives.md), [TensorRT-LLM markdown twin](/tools/nvidia-tensorrt-llm/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-nvidia-tensorrt-llm.md) 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](/tools/ai-hypercomputer-jetstream/trust); [TensorRT-LLM trust report](/tools/nvidia-tensorrt-llm/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/_