Comparison
yalm vs FasterTransformer
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.
Markdown twin · yalm alternatives · FasterTransformer alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | yalm | FasterTransformer |
|---|---|---|
| Maintenance | Slowing (315d since push) As of 1mo · github_public_v1 | Dormant (862d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1mo · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- yalm
- LLM inference engine in C++/CUDA without dependency on external libraries except for I/O
- FasterTransformer
- Transformer related optimization including BERT and GPT
Stars
- yalm
- 592
- FasterTransformer
- 6.4k
Forks
- yalm
- 64
- FasterTransformer
- 935
Open issues
- yalm
- 4
- FasterTransformer
- 289
Language
- yalm
- C++
- FasterTransformer
- C++
Adopt for
- yalm
- 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.
- FasterTransformer
- Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch.
Persona
- yalm
- -
- FasterTransformer
- -
Runtime
- yalm
- -
- FasterTransformer
- -
License
- yalm
- -
- FasterTransformer
- Apache-2.0
Last pushed
- yalm
- Sep 13, 2025
- FasterTransformer
- Mar 27, 2024
Categories
- yalm
- Inference & Serving
- FasterTransformer
- Inference & Serving
Trust and health
Maintenance
- yalm
- Slowing (36%)
- FasterTransformer
- Dormant (18%)
Days since push
- yalm
- 315d
- FasterTransformer
- 862d
Open issues (now)
- yalm
- 4
- FasterTransformer
- 289
Owner type
- yalm
- User
- FasterTransformer
- Organization
Full report
- yalm
- Trust report
- FasterTransformer
- Trust report
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).
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
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 592) - visibility, not fit.
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (andrewkchan/yalm) · observed Jul 25, 2026
- GitHub forks (andrewkchan/yalm) · observed Jul 25, 2026
- Last push (andrewkchan/yalm) · observed Sep 13, 2025
- License file (unknown) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (NVIDIA/FasterTransformer) · observed Aug 7, 2026
- GitHub forks (NVIDIA/FasterTransformer) · observed Aug 7, 2026
- Last push (NVIDIA/FasterTransformer) · observed Mar 27, 2024
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: yalm 592 · FasterTransformer 6.4k (synced Jul 25, 2026).
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 592) - 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 592). 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 and FasterTransformer alternatives (yalm markdown twin, FasterTransformer 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, 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; FasterTransformer trust report.