AlexCheema

AlexCheema / talos-vs-macbook

Public

microGPT benchmarks: a single M4 Max MacBook Pro P-core in C runs Karpathy's 4192-parameter transformer at ~71x the throughput of TALOS-V2's FPGA implementation.

142
9
100% credibility
Found May 03, 2026 at 132 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
C
AI Summary

This repository provides benchmarks comparing the inference speed of a tiny character-level transformer model across Python, NumPy, MLX, and optimized C implementations on Apple Silicon Macs against an FPGA hardware baseline.

How It Works

1
🔍 Discover the speed test

You hear about a fun experiment comparing how fast your MacBook can generate made-up names using a tiny AI compared to special computer hardware.

2
📥 Grab the files

Visit the page and download the simple folder to your Mac's desktop.

3
🔧 Get ready

The folder has everything needed; it grabs the AI's brain files automatically if missing.

4
▶️ Start the magic test

Double-click the 'run' script and watch different ways of running the AI race against each other, printing super-fast speeds right in your terminal.

5
📊 See the winners

Results show plain code is slow, but speedy tuned versions make your MacBook fly way faster than the hardware gadget.

6
😂 Make funny names

Run a quick command to generate silly name ideas like 'kana' or 'keelan' to see the AI in action.

🏆 Your Mac rocks!

You now know your everyday laptop crushes tiny AI tasks with smart coding, feeling smart and amazed.

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 132 to 142 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is talos-vs-macbook?

This repo benchmarks Andrej Karpathy's microGPT—a 4192-parameter character-level transformer from his GitHub gist—across implementations on a single M4 Max MacBook Pro P-core versus TALOS-V2's FPGA setup. Run `./run.sh` on any Apple Silicon Mac to fetch weights, build C binaries, and test pure Python, NumPy, MLX (CPU/GPU), and optimized C versions, generating throughput charts and sample names on Karpathy's names dataset. It reveals how framework overhead kills tiny-model perf, with C hitting 71x TALOS-V2's tok/sec.

Why is it gaining traction?

Developers dig the shock value: NumPy and MLX lag the FPGA due to dispatch costs on 4K-MAC workloads, while C smokes everything on perf-per-watt. One-script reproducibility lets you verify on your MacBook Pro, tweaking N tokens or sampling names via CLI flags. It's a bite-sized reality check on GitHub microGPT hype versus real hardware like Cyclone V FPGAs.

Who should use this?

Embedded engineers benchmarking CPU-vs-FPGA inference for edge devices. Apple Silicon devs tuning tiny LLMs before scaling up. Researchers replicating Karpathy's microGPT py experiments to probe framework limits.

Verdict

Grab it for quick benchmarks if you're on M4/M-series—solid docs and MIT license make it dead simple. At 63 stars and 1.0% credibility score, it's an early proof-of-concept, not production-ready, but killer for sparking optimization debates.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.