Comparison
FasterTransformer vs ggrun
Verdict
Pick FasterTransformer if highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch; pick ggrun if ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities.
Markdown twin · FasterTransformer alternatives · ggrun alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | FasterTransformer | ggrun |
|---|---|---|
| Maintenance | Dormant (862d since push) As of 2w · github_public_v1 | Very active (1d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 1w · 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
- FasterTransformer
- Transformer related optimization including BERT and GPT
- ggrun
- Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server
Stars
- FasterTransformer
- 6.4k
- ggrun
- 264
Forks
- FasterTransformer
- 935
- ggrun
- 14
Open issues
- FasterTransformer
- 289
- ggrun
- 1
Language
- FasterTransformer
- C++
- ggrun
- Go
Adopt for
- FasterTransformer
- Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch.
- ggrun
- ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities.
Persona
- FasterTransformer
- -
- ggrun
- -
Runtime
- FasterTransformer
- -
- ggrun
- -
License
- FasterTransformer
- Apache-2.0
- ggrun
- MIT License allows using ggrun freely in both open source and commercial projects, with conditions that the copyright notice and permission notice are preserved.
Last pushed
- FasterTransformer
- Mar 27, 2024
- ggrun
- Aug 11, 2026
Categories
- FasterTransformer
- Inference & Serving
- ggrun
- Inference & Serving
Trust and health
Maintenance
- FasterTransformer
- Dormant (18%)
- ggrun
- Very active (96%)
Days since push
- FasterTransformer
- 862d
- ggrun
- 1d
Open issues (now)
- FasterTransformer
- 289
- ggrun
- 1
Owner type
- FasterTransformer
- Organization
- ggrun
- User
Full report
- FasterTransformer
- Trust report
- ggrun
- Trust report
Choose FasterTransformer if…
- FasterTransformer is primarily C++; ggrun is Go.
- License: FasterTransformer is Apache-2.0, ggrun is MIT.
- Tags unique to FasterTransformer: bert, cublas, cublaslt, gpt.
- 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 ggrun if…
- ggrun is primarily Go; FasterTransformer is C++.
- License: ggrun is MIT, FasterTransformer is Apache-2.0.
- Pricing: Free to use under MIT license; no direct costs involved in usage..
- Tags unique to ggrun: gguf, golang, inference-server, llama-cpp.
- When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs
When NOT to use ggrun
- For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity
- When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun
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 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 (raketenkater/ggrun) · observed Aug 13, 2026
- GitHub forks (raketenkater/ggrun) · observed Aug 13, 2026
- Last push (raketenkater/ggrun) · observed Aug 11, 2026
- License file (MIT) · observed Aug 13, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: FasterTransformer 6.4k · ggrun 264 (synced Aug 7, 2026).
Common questions
- What is the difference between FasterTransformer and ggrun?
- FasterTransformer: Transformer related optimization including BERT and GPT. ggrun: Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server. See the comparison table for live GitHub stats and shared categories.
- When should I choose FasterTransformer over ggrun?
- Choose FasterTransformer over ggrun when FasterTransformer is primarily C++; ggrun is Go; License: FasterTransformer is Apache-2.0, ggrun is MIT; Tags unique to FasterTransformer: bert, cublas, cublaslt, gpt; When aiming for high performance with GPU-based FP16 computations for BERT or GPT models specifically.
- When should I choose ggrun over FasterTransformer?
- Choose ggrun over FasterTransformer when ggrun is primarily Go; FasterTransformer is C++; License: ggrun is MIT, FasterTransformer is Apache-2.0; Pricing: Free to use under MIT license; no direct costs involved in usage.; Tags unique to ggrun: gguf, golang, inference-server, llama-cpp; When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs.
- 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 ggrun?
- For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun
- Is FasterTransformer or ggrun more popular on GitHub?
- FasterTransformer has more GitHub stars (6,446 vs 264). Stars measure visibility, not whether either tool fits your constraints.
- Are FasterTransformer and ggrun open source?
- Yes - both are open-source projects on GitHub (FasterTransformer: Apache-2.0, ggrun: MIT).
- Where can I find alternatives to FasterTransformer or ggrun?
- GraphCanon lists graph-backed alternatives at FasterTransformer alternatives and ggrun alternatives (FasterTransformer markdown twin, ggrun 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 ggrun?
- FasterTransformer: Dormant. ggrun: Very active. 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 ggrun?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FasterTransformer trust report; ggrun trust report.