--- title: "yalm vs FasterTransformer" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/andrewkchan-yalm-vs-nvidia-fastertransformer" tools: ["andrewkchan-yalm", "nvidia-fastertransformer"] --- # yalm vs FasterTransformer *GraphCanon updated Aug 25, 2026* ## Verdict Pick yalm if yALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries; pick FasterTransformer if highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. [yalm](https://github.com/andrewkchan/yalm) reports 596 GitHub stars, 64 forks, and 4 open issues, last pushed Sep 13, 2025. [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 [yalm's repository](https://github.com/andrewkchan/yalm) and [FasterTransformer's repository](https://github.com/NVIDIA/FasterTransformer). | | [yalm](/tools/andrewkchan-yalm.md) | [FasterTransformer](/tools/nvidia-fastertransformer.md) | | --- | --- | --- | | Tagline | LLM inference engine in C++/CUDA without dependency on external libraries except for I/O | Transformer related optimization including BERT and GPT | | Stars | 596 | 6,446 | | Forks | 64 | 935 | | Open issues | 4 | 289 | | Language | C++ | C++ | | Adopt for | YALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries. | Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [yalm](/tools/andrewkchan-yalm.md) | [FasterTransformer](/tools/nvidia-fastertransformer.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 345d | 862d | | Open issues (now) | 4 | 289 | | Stars delta | +4 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/andrewkchan-yalm/trust.md) | [trust report](/tools/nvidia-fastertransformer/trust.md) | ## Decision facts: yalm - **Adopt for:** YALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries. ## 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 yalm if… - Tags unique to yalm: cpp, llm-inference, machine-learning. - When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies - More recently updated (last pushed Sep 13, 2025). ### Choose FasterTransformer if… - Tags unique to FasterTransformer: bert, cublas, cublaslt, gpt. - When aiming for high performance with GPU-based FP16 computations for BERT or GPT models specifically. - More GitHub stars (6.4k vs 596) - visibility, not fit. ## When NOT to use yalm - If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs - For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope ## 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 yalm and FasterTransformer? yalm: LLM inference engine in C++/CUDA without dependency on external libraries except for I/O. FasterTransformer: Transformer related optimization including BERT and GPT. See the comparison table for live GitHub stats and shared categories. ### When should I choose yalm over FasterTransformer? Choose yalm over FasterTransformer when Tags unique to yalm: cpp, llm-inference, machine-learning; When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies; More recently updated (last pushed Sep 13, 2025). ### When should I choose FasterTransformer over yalm? Choose FasterTransformer over yalm when Tags unique to FasterTransformer: bert, cublas, cublaslt, gpt; When aiming for high performance with GPU-based FP16 computations for BERT or GPT models specifically; More GitHub stars (6.4k vs 596) - visibility, not fit. ### When should I avoid yalm? If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope ### 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 yalm or FasterTransformer more popular on GitHub? FasterTransformer has more GitHub stars (6,446 vs 596). Stars measure visibility, not whether either tool fits your constraints. ### Are yalm and FasterTransformer open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to yalm or FasterTransformer? GraphCanon lists graph-backed alternatives at [yalm alternatives](/tools/andrewkchan-yalm/alternatives) and [FasterTransformer alternatives](/tools/nvidia-fastertransformer/alternatives) ([yalm markdown twin](/tools/andrewkchan-yalm/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/andrewkchan-yalm-vs-nvidia-fastertransformer.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, yalm or FasterTransformer? yalm: Slowing. 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 yalm and FasterTransformer? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [yalm trust report](/tools/andrewkchan-yalm/trust); [FasterTransformer trust report](/tools/nvidia-fastertransformer/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=andrewkchan-yalm`](/api/graphcanon/graph?tool=andrewkchan-yalm) - 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/_