Twitter/X

@0xSero argues that local AI disappointments come mostly from inference…

2026-04-05 · 16:21 UTC ·@0xSero ·0 min read

Brief

@0xSero argues that local AI disappointments come mostly from inference constraints, not from the underlying REAP methodology. He then announces Gemma-4-21B-REAP, claiming it performs well and improves reasoning accuracy. The release targets practical local deployment, with an advertised memory footprint of 12GB VRAM for some context or 16GB for full context.

Source evidence

title: @0xSero: "REAPs are broken"
"Don't make local AI a disappointment"

Blah, blah, blah. Making AI accessible t...
author: @0xSero
contenttype: tweet
publication: Twitter/X
published: 2026-04-05T16:21:03+00:00
sourceurl: https://x.com/0xSero/status/2040827064371073375

word_count: 88

"REAPs are broken"
"Don't make local AI a disappointment"

Blah, blah, blah. Making AI accessible to everyone, never stopping, never slowing down.

AI inference is responsible for the majority of the disappointments people experience. It's not the methodology.

0xSero (@0xSero)

As promised!

Gemma-4-21B-REAP is out! Results are great it held up really well and actually gained accuracy on reasoning tasks.

MLX & GGUF bros do you thing!

This should fit on as little as 12GB of vram with some context, or 16GB with full context

huggingface.co/0xSero/gemma-…

— https://nitter.net/0xSero/status/2040822269400723955#m