--- title: "FasterTransformer vs Star-Attention" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-fastertransformer-vs-nvidia-star-attention" tools: ["nvidia-fastertransformer", "nvidia-star-attention"] --- # FasterTransformer vs Star-Attention *GraphCanon updated Aug 25, 2026* ## Verdict Pick FasterTransformer if highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch; pick Star-Attention if star-Attention specializes in long sequence inference of large language models using star-attention to maintain efficiency. [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) reports 6.4k GitHub stars, 935 forks, and 289 open issues, last pushed Mar 27, 2024. [Star-Attention](https://arxiv.org/abs/2411.17116) has 392 stars, 25 forks, and 1 open issues, last pushed Jun 25, 2025. Figures are from public GitHub metadata via [FasterTransformer's repository](https://github.com/NVIDIA/FasterTransformer) and [Star-Attention's repository](https://github.com/NVIDIA/Star-Attention). | | [FasterTransformer](/tools/nvidia-fastertransformer.md) | [Star-Attention](/tools/nvidia-star-attention.md) | | --- | --- | --- | | Tagline | Transformer related optimization including BERT and GPT | Efficient LLM Inference over Long Sequences | | Stars | 6,446 | 392 | | Forks | 935 | 25 | | Open issues | 289 | 1 | | Language | C++ | Python | | Adopt for | Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. | Star-Attention specializes in long sequence inference of large language models using star-attention to maintain efficiency. | | 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._ | | [FasterTransformer](/tools/nvidia-fastertransformer.md) | [Star-Attention](/tools/nvidia-star-attention.md) | | --- | --- | --- | | Days since push | 862d | 425d | | Open issues (now) | 289 | 1 | | Stars delta | Unknown | 0 (30d) | | Open issues delta | Unknown | +1 (30d) | | Full report | [trust report](/tools/nvidia-fastertransformer/trust.md) | [trust report](/tools/nvidia-star-attention/trust.md) | ## Decision facts: FasterTransformer - **Adopt for:** Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. ## Decision facts: Star-Attention - **Adopt for:** Star-Attention specializes in long sequence inference of large language models using star-attention to maintain efficiency. ## Choose when ### Choose FasterTransformer if… - FasterTransformer is primarily C++; Star-Attention is Python. - Tags unique to FasterTransformer: bert, cublas, cublaslt, cuda. - When aiming for high performance with GPU-based FP16 computations for BERT or GPT models specifically. ### Choose Star-Attention if… - Star-Attention is primarily Python; FasterTransformer is C++. - Tags unique to Star-Attention: attention-mechanism, large language models, llm-inference. - For applications requiring handling very large input sequences ## When NOT to use FasterTransformer - If looking for active development and latest improvements on LLM Inference as NVIDIA recommends TensorRT-LLM over FasterTransformer now. - When specific frameworks not including TensorFlow, PyTorch, or Triton are required. ## When NOT to use Star-Attention - If your use case involves short sequence processing only - In scenarios where traditional attention mechanisms yield adequate results without performance loss ## Common questions ### What is the difference between FasterTransformer and Star-Attention? FasterTransformer: Transformer related optimization including BERT and GPT. Star-Attention: Efficient LLM Inference over Long Sequences. See the comparison table for live GitHub stats and shared categories. ### When should I choose FasterTransformer over Star-Attention? Choose FasterTransformer over Star-Attention when FasterTransformer is primarily C++; Star-Attention is Python; Tags unique to FasterTransformer: bert, cublas, cublaslt, cuda; When aiming for high performance with GPU-based FP16 computations for BERT or GPT models specifically. ### When should I choose Star-Attention over FasterTransformer? Choose Star-Attention over FasterTransformer when Star-Attention is primarily Python; FasterTransformer is C++; Tags unique to Star-Attention: attention-mechanism, large language models, llm-inference; For applications requiring handling very large input sequences. ### When should I avoid FasterTransformer? If looking for active development and latest improvements on LLM Inference as NVIDIA recommends TensorRT-LLM over FasterTransformer now. When specific frameworks not including TensorFlow, PyTorch, or Triton are required. ### When should I avoid Star-Attention? If your use case involves short sequence processing only In scenarios where traditional attention mechanisms yield adequate results without performance loss ### Is FasterTransformer or Star-Attention more popular on GitHub? FasterTransformer has more GitHub stars (6,446 vs 392). Stars measure visibility, not whether either tool fits your constraints. ### Are FasterTransformer and Star-Attention open source? Yes - both are open-source projects on GitHub (FasterTransformer: Apache-2.0, Star-Attention: Apache-2.0). ### Where can I find alternatives to FasterTransformer or Star-Attention? GraphCanon lists graph-backed alternatives at [FasterTransformer alternatives](/tools/nvidia-fastertransformer/alternatives) and [Star-Attention alternatives](/tools/nvidia-star-attention/alternatives) ([FasterTransformer markdown twin](/tools/nvidia-fastertransformer/alternatives.md), [Star-Attention markdown twin](/tools/nvidia-star-attention/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/nvidia-fastertransformer-vs-nvidia-star-attention.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FasterTransformer or Star-Attention? FasterTransformer: Dormant. Star-Attention: Dormant. 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 FasterTransformer and Star-Attention? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FasterTransformer trust report](/tools/nvidia-fastertransformer/trust); [Star-Attention trust report](/tools/nvidia-star-attention/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-fastertransformer`](/api/graphcanon/graph?tool=nvidia-fastertransformer) - 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/_