SemiAnalysisAI/InferenceX
Open Source AI Accelerator Research Platform Standard / 开源推理研究平台
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
1 release in the last 90 days, the latest 78 days ago, with 112 contributors on GitHub.
Use Cases & Benefits
- Runs continuous, automated benchmarks of popular open-source LLM inference frameworks to track real-time performance improvements.
- Provides near real-time, reproducible benchmarking results that reflect the latest software and hardware optimizations for inference workloads.
- Use for evaluating and comparing inference performance across different GPUs and software stacks in machine learning research.
- Use for monitoring ongoing software improvements in inference engines to optimize deployment in production ML systems.
- Use for making informed hardware and software purchasing decisions in datacenter environments focused on LLM inference efficiency.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
InferenceX™ (formerly InferenceMAX) runs our suite of benchmarks every night, continually re-benchmarking the world’s most popular open-source inference frameworks used by major token factories and models to track real performance in real time. As these software stacks improve, InferenceX™ captures that progress in near real-time, providing a live indicator of inference performance progress. A live dashboard is available for free publicly at https://inferencex.com/.
InferenceX™, an open-source, under Apache2 license, automated benchmark designed to move at the same rapid speed as the software ecosystem itself, is built to address this challenge.
LLM Inference performance is driven by two pillars, hardware and software. While hardware innovation drives step jumps in performance every year through the release of new GPUs/XPUs and new systems, software evolves every single day, delivering continuous performance gains on top of these step jumps. Speed is the Moat 🚀
AI software like SGLang, vLLM, TensorRT-LLM, CUDA, ROCm and achieve this continuous improvement in performance through kernel-level optimizations, distributed inference strategies, and scheduling innovations that increase the pareto frontier of performance in incremental releases that can be just days apart.
This pace of software advancement creates a challenge: benchmarks conducted at a fixed point in time quickly go stale and do not represent the performance that can be achieved with the latest software packages.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
358+108
Open issues
285+87
Compared with peers in AI
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| thedotmack/claude-mem | 113.6K | +8.8K | 35 | today |
| D4Vinci/Scrapling | 98.1K | +6.9K | 65 | 1 day ago |
| Leonxlnx/taste-skill | 110.7K | +5.2K | 55 | 1 day ago |
| calesthio/OpenMontage | 77.2K | +4.2K | 40 | — |
| heygen-com/hyperframes | 79.6K | +4.1K | 45 | today |
| SemiAnalysisAI/InferenceX | 2.0K | — | 56 | 78 days ago |
| Category median | 98.1K | +5.2K | 45 | — |
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