--- title: "Medusa vs FasterTransformer" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fasterdecoding-medusa-vs-nvidia-fastertransformer" tools: ["fasterdecoding-medusa", "nvidia-fastertransformer"] --- # Medusa vs FasterTransformer *GraphCanon updated Aug 24, 2026* ## Verdict Pick Medusa if medusa enables quicker language model inference with parallel decoding strategies; pick FasterTransformer if highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. [Medusa](https://sites.google.com/view/medusa-llm) reports 2.8k GitHub stars, 205 forks, and 57 open issues, last pushed Jun 25, 2024. [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) has 6.4k stars, 935 forks, and 289 open issues, last pushed Mar 27, 2024. Figures are from public GitHub metadata via [Medusa's repository](https://github.com/FasterDecoding/Medusa) and [FasterTransformer's repository](https://github.com/NVIDIA/FasterTransformer). | | [Medusa](/tools/fasterdecoding-medusa.md) | [FasterTransformer](/tools/nvidia-fastertransformer.md) | | --- | --- | --- | | Tagline | Framework for accelerating LLM generation using multiple decoding heads | Transformer related optimization including BERT and GPT | | Stars | 2,767 | 6,446 | | Forks | 205 | 935 | | Open issues | 57 | 289 | | Language | Jupyter Notebook | C++ | | Adopt for | Medusa enables quicker language model inference with parallel decoding strategies. | Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. | | 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._ | | [Medusa](/tools/fasterdecoding-medusa.md) | [FasterTransformer](/tools/nvidia-fastertransformer.md) | | --- | --- | --- | | Days since push | 790d | 862d | | Open issues (now) | 57 | 289 | | Stars delta | +9 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/fasterdecoding-medusa/trust.md) | [trust report](/tools/nvidia-fastertransformer/trust.md) | ## Decision facts: Medusa - **Adopt for:** Medusa enables quicker language model inference with parallel decoding strategies. ## Decision facts: FasterTransformer - **Adopt for:** Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. ## Choose when ### Choose Medusa if… - Medusa is primarily Jupyter Notebook; FasterTransformer is C++. - Tags unique to Medusa: acceleration, decoding, inference, llm. - When you need to accelerate inference times for large language models without compromising on output quality. ### Choose FasterTransformer if… - FasterTransformer is primarily C++; Medusa is Jupyter Notebook. - 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 NOT to use Medusa - If your model does not benefit from parallelized decoding, such as when the model architecture inherently limits parallel execution efficiency. - In scenarios where the computational resources required for multiple decoding heads exceed what is available or cost-effective within your infrastructure. ## 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. ## Common questions ### What is the difference between Medusa and FasterTransformer? Medusa: Framework for accelerating LLM generation using multiple decoding heads. FasterTransformer: Transformer related optimization including BERT and GPT. See the comparison table for live GitHub stats and shared categories. ### When should I choose Medusa over FasterTransformer? Choose Medusa over FasterTransformer when Medusa is primarily Jupyter Notebook; FasterTransformer is C++; Tags unique to Medusa: acceleration, decoding, inference, llm; When you need to accelerate inference times for large language models without compromising on output quality. ### When should I choose FasterTransformer over Medusa? Choose FasterTransformer over Medusa when FasterTransformer is primarily C++; Medusa is Jupyter Notebook; 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 avoid Medusa? If your model does not benefit from parallelized decoding, such as when the model architecture inherently limits parallel execution efficiency. In scenarios where the computational resources required for multiple decoding heads exceed what is available or cost-effective within your infrastructure. ### 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. ### Is Medusa or FasterTransformer more popular on GitHub? FasterTransformer has more GitHub stars (6,446 vs 2,767). Stars measure visibility, not whether either tool fits your constraints. ### Are Medusa and FasterTransformer open source? Yes - both are open-source projects on GitHub (Medusa: Apache-2.0, FasterTransformer: Apache-2.0). ### Where can I find alternatives to Medusa or FasterTransformer? GraphCanon lists graph-backed alternatives at [Medusa alternatives](/tools/fasterdecoding-medusa/alternatives) and [FasterTransformer alternatives](/tools/nvidia-fastertransformer/alternatives) ([Medusa markdown twin](/tools/fasterdecoding-medusa/alternatives.md), [FasterTransformer markdown twin](/tools/nvidia-fastertransformer/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/fasterdecoding-medusa-vs-nvidia-fastertransformer.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Medusa or FasterTransformer? Medusa: Dormant. FasterTransformer: 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 Medusa and FasterTransformer? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Medusa trust report](/tools/fasterdecoding-medusa/trust); [FasterTransformer trust report](/tools/nvidia-fastertransformer/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fasterdecoding-medusa`](/api/graphcanon/graph?tool=fasterdecoding-medusa) - 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/_