AnkitNayak-eth

AnkitNayak-eth / llmBench

Public

llmBench is a high-depth benchmarking tool designed to measure the raw performance of local LLM runtimes (Ollama, llama.cpp) while providing deep hardware intelligence.

ai-benchmark llm llm-inference llm-tools local-llm
11
1
100% credibility
Found Mar 16, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

llmBench is a tool for analyzing computer hardware details and measuring the speed and efficiency of locally running AI language models, with comparisons to worldwide rankings.

How It Works

1
🔍 Find llmBench

You hear about this tool that helps everyday folks test how fast their home computer runs AI chatbots.

2
📥 Grab and launch it

Download the files, set up a simple folder, and start the program with one easy run.

3
🖥️ Discover your computer's strengths

It gently checks your processor, memory, graphics, and more, displaying everything in colorful, easy-to-read tables.

4
Pick your focus
🔧
Hardware tips

See personalized suggestions for AI models that fit your computer's power perfectly.

🚀
Run a speed test

Test how quickly your AI responds in a real conversation.

5
🤖 Choose your AI helper

From the list of ready AI chatbots on your computer, pick the one to try.

6
📊 See speeds and rankings

Watch as it chats with the AI, measuring response time, power use, heat, and how it stacks up worldwide.

🏆 Own your results

Celebrate with a detailed report showing your computer's AI performance, tips, and global standing—saved for keeps.

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Star Growth

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AI-Generated Review

What is llmBench?

llmBench is a Python-based llm benchmark tool designed for high-depth benchmarking of local LLM runtimes like Ollama and llama.cpp. It measures raw performance metrics—tokens per second, TTFT, VRAM delta, thermal velocity, joules per token—while providing deep hardware intelligence on CPU caches, RAM speeds, GPU PCIe links, and power limits. Run it via a simple CLI with two modes: hardware forensics with model recommendations, or full inference stress-tests mapped to the LMSYS Arena leaderboard.

Why is it gaining traction?

Unlike basic lmbench-style tools, llmBench delivers parametric Arena score estimates, turning local tokens/sec into global ELO rankings, plus VRAM-optimized Ollama and GGUF suggestions tailored to your rig. Developers notice the rich CLI tables, real-time NVIDIA thermals, and JSON reports for tracking runs. The live leaderboard scraping keeps baselines fresh without manual tweaks.

Who should use this?

AI engineers deploying Ollama or llama.cpp on NVIDIA Windows setups for inference tuning. Hardware tinkerers auditing rigs for LLM bottlenecks like PCIe bandwidth or DDR5 speeds. Local LLM hobbyists comparing their 7B model speeds to frontier leaders before scaling.

Verdict

Worth a quick test if you're on Windows with NVIDIA—solid docs and zero-setup CLI make it accessible despite 11 stars and 1.0% credibility signaling early maturity. Lacks broad OS support and tests, so fork and contribute if it clicks.

(198 words)

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