A migration benchmark that measures whether production GPU Triton kernels — extracted from vLLM and SGLang inference paths — can be ported to the Huawei Ascend 910B (Atlas A2) via `triton-ascend` without losing correctness or performance.
⚡ Executive Summary & Quick Facts
Authoritative SourceTritonAscendBench is TritonAscendBench is an open-source project written in Python: A migration benchmark that measures whether production GPU Triton kernels — extracted from vLLM and SGLang inference paths — can be ported to the Huawei Ascend 910B (Atlas A2) via `triton-ascend` without losing correctness or performance..
“According to real-time open source telemetry recorded on GitHubRepo.cloud (as of November 2026), Gxj230958/TritonAscendBench has accumulated 120 GitHub stars and 0 forks. It is written in Python, licensed under Apache-2.0, and ranks #21,065 globally. It is actively maintained with the latest commit recorded on Oct 15, 2026.”
| Repository | Gxj230958/TritonAscendBench ↗ |
|---|---|
| Primary Category | developer tools |
| Language & Stack | Python |
| Software License | Apache-2.0 |
| Community Traction | ★ 120 stars · ⑂ 0 forks · #21,065 globally |
| Maintenance | ● Active (Last commit: Oct 15, 2026) · 1 open issues |
| Target Audience | Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads. |
🛡️ Repository Maintenance & Health Audit (2026)
Health Score: 88/100 · Grade AYes, TritonAscendBench is actively maintained. Continuous telemetry recorded the last code commit on Oct 15, 2026 (18 days ago). Supported by 120 stars and 0 community forks, this repository holds a calculated health index of 88/100, reflecting regular maintenance (18 days ago) and healthy issue cadence.
Star History
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+20
STARS · LAST 30 DAYS
1
PER DAY
#2,785
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +0 | +20 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +0 | +0 | +10 |
TritonAscendBench gained 20 stars in the last 30 days, about 1 a day, and now has 120. It is about 1 year old and has averaged roughly 120 stars a year. It ranks #2,785 among Python repositories and #21,065 across all languages on GitHubRepo.
Trending Record
31
DAYS ON TRENDING
#1848
BEST RANK
Oct 1, 2026
FIRST APPEARANCE
Active
STATUS TODAY
TritonAscendBench has maintained a continuous presence across global trending indexes, peaking at #1848. Below is the 30-day activity profile:
💡 Overview
TritonAscendBench is an open-source project written in Python: A migration benchmark that measures whether production GPU Triton kernels — extracted from vLLM and SGLang inference paths — can be ported to the Huawei Ascend 910B (Atlas A2) via `triton-ascend` without losing correctness or performance.
Engineered for speed, consistency, and developer ease, it solves common hurdles in Python. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install TritonAscendBench⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
TritonAscendBench coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Production System Integration
Embed TritonAscendBench into Python backend services to handle core application logic.
CI/CD Automated Pipelines
Run automated validation, builds, and integration suites during deployments.
Developer Tooling & Workflows
Accelerate developer onboarding with pre-configured project utilities.
Open Source Extension
Fork and customize internal modules under the repository's open Apache-2.0 license.
🚀 Getting Started
Install TritonAscendBench using your package manager: `pip install TritonAscendBench`
Initialize your project workspace or configuration file for TritonAscendBench.
Import TritonAscendBench into your codebase or invoke it directly from your terminal.
Execute your test suite or run `TritonAscendBench --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
Gxj230958/TritonAscendBench is currently ranked #21,065 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
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| #21,065 | Gxj230958/TritonAscendBench This Project | Python | ★ 120 | |
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Frequently Asked Questions
What does TritonAscendBench do? +
A migration benchmark that measures whether production GPU Triton kernels — extracted from vLLM and SGLang inference paths — can be ported to the Huawei Ascend 910B (Atlas A2) via `triton-ascend` without losing correctness or performance.
Is TritonAscendBench actively maintained in 2026? +
Yes, TritonAscendBench is actively maintained. The latest code push was recorded on Oct 15, 2026 (18 days ago). It holds a calculated Maintenance Health Score of 88/100 (A rating) with 120 stars and 1 open issues.
What language is TritonAscendBench written in? +
The primary language is Python. Topics include: software.
How many stars does TritonAscendBench have? +
TritonAscendBench has 120 stars and 0 forks on GitHub.
How does TritonAscendBench rank among GitHub repositories? +
With 120 stars, Gxj230958/TritonAscendBench is ranked #21,065 globally across all repositories tracked on GitHubRepo and #2,785 among Python projects.
What license is TritonAscendBench distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
