--- title: "bitsandbytes vs FasterTransformer" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bitsandbytes-foundation-bitsandbytes-vs-nvidia-fastertransformer" tools: ["bitsandbytes-foundation-bitsandbytes", "nvidia-fastertransformer"] --- # bitsandbytes vs FasterTransformer *GraphCanon updated Aug 7, 2026* ## Verdict Pick bitsandbytes if bitsandbytes provides k-bit quantization in PyTorch, enhancing large language model accessibility across multiple hardware platforms; pick FasterTransformer if highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. [bitsandbytes](https://huggingface.co/docs/bitsandbytes/main/en/index) reports 8.4k GitHub stars, 900 forks, and 54 open issues, last pushed Jul 29, 2026. [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 [bitsandbytes's repository](https://github.com/bitsandbytes-foundation/bitsandbytes) and [FasterTransformer's repository](https://github.com/NVIDIA/FasterTransformer). | | [bitsandbytes](/tools/bitsandbytes-foundation-bitsandbytes.md) | [FasterTransformer](/tools/nvidia-fastertransformer.md) | | --- | --- | --- | | Tagline | Large language model quantization toolkit for PyTorch. | Transformer related optimization including BERT and GPT | | Stars | 8,385 | 6,446 | | Forks | 900 | 935 | | Open issues | 54 | 289 | | Language | Python | C++ | | Adopt for | bitsandbytes provides k-bit quantization in PyTorch, enhancing large language model accessibility across multiple hardware platforms. | Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bitsandbytes](/tools/bitsandbytes-foundation-bitsandbytes.md) | [FasterTransformer](/tools/nvidia-fastertransformer.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 862d | | Open issues (now) | 54 | 289 | | Full report | [trust report](/tools/bitsandbytes-foundation-bitsandbytes/trust.md) | [trust report](/tools/nvidia-fastertransformer/trust.md) | ## Decision facts: bitsandbytes - **Adopt for:** bitsandbytes provides k-bit quantization in PyTorch, enhancing large language model accessibility across multiple hardware platforms. ## 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 bitsandbytes if… - bitsandbytes is primarily Python; FasterTransformer is C++. - License: bitsandbytes is MIT, FasterTransformer is Apache-2.0. - Tags unique to bitsandbytes: llm, machine-learning, qlora, quantization. - Also covers LLM Frameworks. - When you need advanced k-bit quantization on PyTorch for hardware like NVIDIA GPUs with SM75+ recommended. ### Choose FasterTransformer if… - FasterTransformer is primarily C++; bitsandbytes is Python. - License: FasterTransformer is Apache-2.0, bitsandbytes is MIT. - 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 bitsandbytes - Avoid if your setup includes Intel Gaudi processors as QLoRA 4-bit support is partial and 8-bit optimizers are not available. - Steer clear if you require full compatibility with ARM-based CPUs, as specific GPU optimizations might lack coverage. ## 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 bitsandbytes and FasterTransformer? bitsandbytes: Large language model quantization toolkit for PyTorch.. FasterTransformer: Transformer related optimization including BERT and GPT. See the comparison table for live GitHub stats and shared categories. ### When should I choose bitsandbytes over FasterTransformer? Choose bitsandbytes over FasterTransformer when bitsandbytes is primarily Python; FasterTransformer is C++; License: bitsandbytes is MIT, FasterTransformer is Apache-2.0; Tags unique to bitsandbytes: llm, machine-learning, qlora, quantization; Also covers LLM Frameworks; When you need advanced k-bit quantization on PyTorch for hardware like NVIDIA GPUs with SM75+ recommended. ### When should I choose FasterTransformer over bitsandbytes? Choose FasterTransformer over bitsandbytes when FasterTransformer is primarily C++; bitsandbytes is Python; License: FasterTransformer is Apache-2.0, bitsandbytes is MIT; 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 bitsandbytes? Avoid if your setup includes Intel Gaudi processors as QLoRA 4-bit support is partial and 8-bit optimizers are not available. Steer clear if you require full compatibility with ARM-based CPUs, as specific GPU optimizations might lack coverage. ### 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 bitsandbytes or FasterTransformer more popular on GitHub? bitsandbytes has more GitHub stars (8,385 vs 6,446). Stars measure visibility, not whether either tool fits your constraints. ### Are bitsandbytes and FasterTransformer open source? Yes - both are open-source projects on GitHub (bitsandbytes: MIT, FasterTransformer: Apache-2.0). ### Where can I find alternatives to bitsandbytes or FasterTransformer? GraphCanon lists graph-backed alternatives at [bitsandbytes alternatives](/tools/bitsandbytes-foundation-bitsandbytes/alternatives) and [FasterTransformer alternatives](/tools/nvidia-fastertransformer/alternatives) ([bitsandbytes markdown twin](/tools/bitsandbytes-foundation-bitsandbytes/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/bitsandbytes-foundation-bitsandbytes-vs-nvidia-fastertransformer.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, bitsandbytes or FasterTransformer? bitsandbytes: Very active. 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 bitsandbytes and FasterTransformer? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [bitsandbytes trust report](/tools/bitsandbytes-foundation-bitsandbytes/trust); [FasterTransformer trust report](/tools/nvidia-fastertransformer/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bitsandbytes-foundation-bitsandbytes`](/api/graphcanon/graph?tool=bitsandbytes-foundation-bitsandbytes) - 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/_