--- title: "FasterTransformer vs ggrun" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-fastertransformer-vs-raketenkater-ggrun" tools: ["nvidia-fastertransformer", "raketenkater-ggrun"] --- # FasterTransformer vs ggrun *GraphCanon updated Aug 13, 2026* ## Verdict Pick FasterTransformer if highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch; pick ggrun if ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities. [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) reports 6.4k GitHub stars, 935 forks, and 289 open issues, last pushed Mar 27, 2024. [ggrun](https://github.com/raketenkater/ggrun) has 264 stars, 14 forks, and 1 open issues, last pushed Aug 11, 2026. Figures are from public GitHub metadata via [FasterTransformer's repository](https://github.com/NVIDIA/FasterTransformer) and [ggrun's repository](https://github.com/raketenkater/ggrun). | | [FasterTransformer](/tools/nvidia-fastertransformer.md) | [ggrun](/tools/raketenkater-ggrun.md) | | --- | --- | --- | | Tagline | Transformer related optimization including BERT and GPT | Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server | | Stars | 6,446 | 264 | | Forks | 935 | 14 | | Open issues | 289 | 1 | | Language | C++ | Go | | Adopt for | Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. | ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT License allows using ggrun freely in both open source and commercial projects, with conditions that the copyright notice and permission notice are preserved. | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FasterTransformer](/tools/nvidia-fastertransformer.md) | [ggrun](/tools/raketenkater-ggrun.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 862d | 1d | | Open issues (now) | 289 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/nvidia-fastertransformer/trust.md) | [trust report](/tools/raketenkater-ggrun/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: ggrun - **Pricing:** freemium - Free to use under MIT license; no direct costs involved in usage. - **Adopt for:** ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities. - **License detail:** MIT License allows using ggrun freely in both open source and commercial projects, with conditions that the copyright notice and permission notice are preserved. ## Choose when ### Choose FasterTransformer if… - FasterTransformer is primarily C++; ggrun is Go. - License: FasterTransformer is Apache-2.0, ggrun is MIT. - Tags unique to FasterTransformer: bert, cublas, cublaslt, gpt. - When aiming for high performance with GPU-based FP16 computations for BERT or GPT models specifically. ### Choose ggrun if… - ggrun is primarily Go; FasterTransformer is C++. - License: ggrun is MIT, FasterTransformer is Apache-2.0. - Pricing: Free to use under MIT license; no direct costs involved in usage.. - Tags unique to ggrun: gguf, golang, inference-server, llama-cpp. - When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs ## 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 ggrun - For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity - When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun ## Common questions ### What is the difference between FasterTransformer and ggrun? FasterTransformer: Transformer related optimization including BERT and GPT. ggrun: Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server. See the comparison table for live GitHub stats and shared categories. ### When should I choose FasterTransformer over ggrun? Choose FasterTransformer over ggrun when FasterTransformer is primarily C++; ggrun is Go; License: FasterTransformer is Apache-2.0, ggrun is MIT; Tags unique to FasterTransformer: bert, cublas, cublaslt, gpt; When aiming for high performance with GPU-based FP16 computations for BERT or GPT models specifically. ### When should I choose ggrun over FasterTransformer? Choose ggrun over FasterTransformer when ggrun is primarily Go; FasterTransformer is C++; License: ggrun is MIT, FasterTransformer is Apache-2.0; Pricing: Free to use under MIT license; no direct costs involved in usage.; Tags unique to ggrun: gguf, golang, inference-server, llama-cpp; When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs. ### 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 ggrun? For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun ### Is FasterTransformer or ggrun more popular on GitHub? FasterTransformer has more GitHub stars (6,446 vs 264). Stars measure visibility, not whether either tool fits your constraints. ### Are FasterTransformer and ggrun open source? Yes - both are open-source projects on GitHub (FasterTransformer: Apache-2.0, ggrun: MIT). ### Where can I find alternatives to FasterTransformer or ggrun? GraphCanon lists graph-backed alternatives at [FasterTransformer alternatives](/tools/nvidia-fastertransformer/alternatives) and [ggrun alternatives](/tools/raketenkater-ggrun/alternatives) ([FasterTransformer markdown twin](/tools/nvidia-fastertransformer/alternatives.md), [ggrun markdown twin](/tools/raketenkater-ggrun/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-raketenkater-ggrun.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FasterTransformer or ggrun? FasterTransformer: Dormant. ggrun: 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 FasterTransformer and ggrun? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FasterTransformer trust report](/tools/nvidia-fastertransformer/trust); [ggrun trust report](/tools/raketenkater-ggrun/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/_