No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by aidatatools · Agent Tool · ★ 389
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ollama-benchmark safe to install? View the security audit →
llm-benchmark (ollama-benchmark) LLM Benchmark for Throughput via Ollama (Local LLMs) Measure how fast your local LLMs really are—with a simple, cross-platform CLI tool that tells you the tokens-per-second truth. Installation prerequisites Working Ollama installation. To create a virtual environment via python3 -m venv To create a virtual environment via uv (For uv virtual environments (recommended for Python 3.13)) Installation Steps Depending on your python setup either or or uv Usage for general users directly Installation and Usage in Video format It's tested on Python 3.10 and above. ollama installation with the following models installed 7B model can be run on machines with 8GB of RAM 13B model can be run on machines with 16GB of RAM Usage explaination On Windows, Linux, and macOS, it will detect memory RAM size to first download required LLM models. When memory RAM size is great
| Stars | 389 |
| Forks | 47 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 73.0424832860828/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-18 |
| Created | 2024-01-18 |
| Platforms | python |
| Est. Tokens | ~14k |
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ollama-benchmark is LLM Benchmark for Throughput via Ollama (Local LLMs). It is categorized as a Agent Tool with 389 GitHub stars.
ollama-benchmark is primarily written in Python. It covers topics such as ai, ai-tools, benchmark.
You can find installation instructions and usage details in the ollama-benchmark GitHub repository at github.com/aidatatools/ollama-benchmark. The project has 389 stars and 47 forks, indicating an active community.
ollama-benchmark is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ollama-benchmark on Agent Skills Hub include py-gpt, npcpy, tools. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: