Comparison
FasterTransformer vs quant.cpp
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.
Markdown twin · FasterTransformer alternatives · quant.cpp alternatives
GraphCanon updated Sep 20, 2026
6views this month
vs
Trust & integrity
| Signal | FasterTransformer | quant.cpp |
|---|---|---|
| Maintenance | Dormant (893d since push) As of Sep 7, 2026 · github_public_v1 | Slowing (146d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 7, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- FasterTransformer
- Transformer related optimization including BERT and GPT
- quant.cpp
- LLM inference with extended context using C
Stars
- FasterTransformer
- 6.4k
- quant.cpp
- 402
Forks
- FasterTransformer
- 934
- quant.cpp
- 44
Open issues
- FasterTransformer
- 289
- quant.cpp
- 11
Language
- FasterTransformer
- C++
- quant.cpp
- C
Adopt for
- FasterTransformer
- Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch.
- quant.cpp
- quant.cpp, a lossless KV cache compression and quantization tool for LLM inference in pure C without dependencies.
Persona
- FasterTransformer
- -
- quant.cpp
- -
Runtime
- FasterTransformer
- -
- quant.cpp
- -
License
- FasterTransformer
- Apache-2.0
- quant.cpp
- Quant.cpp uses the Apache-2.0 license, which allows for free use, modification, and distribution. Contributions to its codebase are welcomed.
Last pushed
- FasterTransformer
- Mar 27, 2024
- quant.cpp
- Apr 26, 2026
Categories
- FasterTransformer
- Inference & Serving
- quant.cpp
- Inference & Serving
Trust and health
Maintenance
- FasterTransformer
- Dormant (18%)
- quant.cpp
- Slowing (36%)
Days since push
- FasterTransformer
- 893d
- quant.cpp
- 146d
Open issues (now)
- FasterTransformer
- 289
- quant.cpp
- 11
Stars delta
- FasterTransformer
- +2 (30d)
- quant.cpp
- +7 (30d)
Full report
- FasterTransformer
- Trust report
- quant.cpp
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (NVIDIA/FasterTransformer) · observed Sep 20, 2026
- GitHub forks (NVIDIA/FasterTransformer) · observed Sep 20, 2026
- Last push (NVIDIA/FasterTransformer) · observed Mar 27, 2024
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (quantumaikr/quant.cpp) · observed Sep 20, 2026
- GitHub forks (quantumaikr/quant.cpp) · observed Sep 20, 2026
- Last push (quantumaikr/quant.cpp) · observed Apr 26, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: FasterTransformer 6.4k · quant.cpp 402 (synced Sep 20, 2026).
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 and quant.cpp alternatives (FasterTransformer markdown twin, quant.cpp markdown twin), 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 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; quant.cpp trust report.