--- title: "FasterTransformer vs quant.cpp" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-fastertransformer-vs-quantumaikr-quant-cpp" tools: ["nvidia-fastertransformer", "quantumaikr-quant-cpp"] --- # FasterTransformer vs quant.cpp *GraphCanon updated Sep 20, 2026* ## Verdict Pick FasterTransformer if highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch; pick quant.cpp if quant.cpp, a lossless KV cache compression and quantization tool for LLM inference in pure C without dependencies. [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) reports 6.4k GitHub stars, 934 forks, and 289 open issues, last pushed Mar 27, 2024. [quant.cpp](https://github.com/quantumaikr/quant.cpp) has 402 stars, 44 forks, and 11 open issues, last pushed Apr 26, 2026. Figures are from public GitHub metadata via [FasterTransformer's repository](https://github.com/NVIDIA/FasterTransformer) and [quant.cpp's repository](https://github.com/quantumaikr/quant.cpp). | | [FasterTransformer](/tools/nvidia-fastertransformer.md) | [quant.cpp](/tools/quantumaikr-quant-cpp.md) | | --- | --- | --- | | Tagline | Transformer related optimization including BERT and GPT | LLM inference with extended context using C | | Stars | 6,448 | 402 | | Forks | 934 | 44 | | Open issues | 289 | 11 | | Language | C++ | C | | Adopt for | Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. | quant.cpp, a lossless KV cache compression and quantization tool for LLM inference in pure C without dependencies. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Quant.cpp uses the Apache-2.0 license, which allows for free use, modification, and distribution. Contributions to its codebase are welcomed. | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FasterTransformer](/tools/nvidia-fastertransformer.md) | [quant.cpp](/tools/quantumaikr-quant-cpp.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 893d | 146d | | Open issues (now) | 289 | 11 | | Stars delta | +2 (30d) | +7 (30d) | | Full report | [trust report](/tools/nvidia-fastertransformer/trust.md) | [trust report](/tools/quantumaikr-quant-cpp/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: quant.cpp - **Requirements:** Requires a C compiler compatible with quant.cpp source code. - **Adopt for:** quant.cpp, a lossless KV cache compression and quantization tool for LLM inference in pure C without dependencies. - **License detail:** Quant.cpp uses the Apache-2.0 license, which allows for free use, modification, and distribution. Contributions to its codebase are welcomed. ## Choose when ### Choose FasterTransformer if… - FasterTransformer is primarily C++; quant.cpp is C. - 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 quant.cpp if… - quant.cpp is primarily C; FasterTransformer is C++. - Requirements: Requires a C compiler compatible with quant.cpp source code.. - Tags unique to quant.cpp: delta-compression, embeddable, gguf, kv-cache. - quant.cpp ships Docker support for self-hosted deployment. - Use quant.cpp when you need extended context for LLM inference in a lightweight, embeddable environment with no external dependencies. ## 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 quant.cpp - Avoid using quant.cpp for projects requiring non-C language support or frameworks since it strictly operates within the context of pure C. - Do not use quant.cpp in environments where rapid runtime performance is paramount and additional compile-time overhead introduced by its unique compression techniques may cause delays. ## Common questions ### What is the difference between FasterTransformer and quant.cpp? FasterTransformer: Transformer related optimization including BERT and GPT. quant.cpp: LLM inference with extended context using C. See the comparison table for live GitHub stats and shared categories. ### When should I choose FasterTransformer over quant.cpp? Choose FasterTransformer over quant.cpp when FasterTransformer is primarily C++; quant.cpp is C; 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 quant.cpp over FasterTransformer? Choose quant.cpp over FasterTransformer when quant.cpp is primarily C; FasterTransformer is C++; Requirements: Requires a C compiler compatible with quant.cpp source code.; Tags unique to quant.cpp: delta-compression, embeddable, gguf, kv-cache; quant.cpp ships Docker support for self-hosted deployment; Use quant.cpp when you need extended context for LLM inference in a lightweight, embeddable environment with no external dependencies. ### 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 quant.cpp? Avoid using quant.cpp for projects requiring non-C language support or frameworks since it strictly operates within the context of pure C. Do not use quant.cpp in environments where rapid runtime performance is paramount and additional compile-time overhead introduced by its unique compression techniques may cause delays. ### Is FasterTransformer or quant.cpp more popular on GitHub? FasterTransformer has more GitHub stars (6,448 vs 402). Stars measure visibility, not whether either tool fits your constraints. ### Are FasterTransformer and quant.cpp open source? Yes - both are open-source projects on GitHub (FasterTransformer: Apache-2.0, quant.cpp: Apache-2.0). ### Where can I find alternatives to FasterTransformer or quant.cpp? GraphCanon lists graph-backed alternatives at [FasterTransformer alternatives](/tools/nvidia-fastertransformer/alternatives) and [quant.cpp alternatives](/tools/quantumaikr-quant-cpp/alternatives) ([FasterTransformer markdown twin](/tools/nvidia-fastertransformer/alternatives.md), [quant.cpp markdown twin](/tools/quantumaikr-quant-cpp/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-quantumaikr-quant-cpp.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FasterTransformer or quant.cpp? FasterTransformer: Dormant. quant.cpp: Slowing. 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 quant.cpp? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FasterTransformer trust report](/tools/nvidia-fastertransformer/trust); [quant.cpp trust report](/tools/quantumaikr-quant-cpp/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/_