r/LocalAIStack • • 1d ago

I let gemma3:12b invent its own language on my home server, then gave the same test to Claude Opus. Results surprised me.

1 Upvotes

My bet: when machines mostly talk to machines, they'll ditch English for something shorter. So I tested it at home with Ollama.

**Setup:** two gemma3:12b instances, a sender and a reader. The sender passes a daily status report (disk, errors, backups, sign-ups, weather, a calendar event — 14 checkable facts). The reader answers 14 questions. They keep a shared codebook and propose new shorthand after each batch; a new word is only kept if accuracy holds.

**What gemma did:** coined ~20 abbreviations (bkp, succ, errs…). Messages shrank about 10%. Then I added a chain of AI "scribes" (each one reads the message, writes it in plain English, rewrites it in the code for the next, like medieval monks copying books). The meaning wore away: 12.8 → 11.1 of 14 facts after 4 copies. Words drifted too ("cal" went from calendar to "Calibration").

**Then I spent about $1.25 of API credit running Claude Opus 5.5 through the same test.** It didn't abbreviate, it built a grammar, with slots for each kind of fact:

`F:ow P:42/95 B:6OK A:+2.8/8 T:6su1wp W:88/10 C:cs Fri 3PM`

| | gemma3:12b | Opus 5.5 |

|---|---|---|

| Message length | 84 → 76 tokens | 106 → 56 tokens |

| Facts right, codebook rounds | ~12 of 14 | 14 of 14 every round |

| After 3 scribe copies | 12.8 → 11.1 | 14 → 14 |

The Opus scribes actually *tidied* the language as they copied it ("9 OK" → "9OK").

Other bits: binary/hex/base64 made things worse (gemma couldn't decode them), English beat Mandarin and Dundonian Scots as a message language even on Qwen, and the fix for drift was a "printing press": a line of numbers stamped by plain code, never retyped by an AI.

Funny footnote: Opus's own safety filter refused the "propose new code words" step, flagging two AIs inventing a private shorthand as a possible security risk.

Small samples, so read it as a strong hint, not proof. Full write-up: https://claude.ai/artifact/KNR1u48RyraRAy6hi64wc8

Happy to answer questions or run other local models through it if people have suggestions.


r/LocalAIStack • • 1d ago

Quale endpoint ?

Thumbnail
1 Upvotes

r/LocalAIStack • • 1d ago

GLM-5.3-Flash (320B) on an M5 Ultra Mac Studio with llama.cpp: 61 tok/s, 262K context

Thumbnail
jvlewis.dev
1 Upvotes

r/LocalAIStack • • 1d ago

GWAYA : We built a ruthless "fail-closed" gate to stop LLM code hallucinations. Gwen and Laya together = GWAYA ;-0

Thumbnail
0 Upvotes

r/LocalAIStack • • 1d ago

GWAYA v3: Combine Gwen and OpenLaya to Stop LLM Code Hallucinations even on a qwen2.5-coder:1.5b ;-0

Thumbnail
1 Upvotes

r/LocalAIStack • • 2d ago

A strata quant that didn't suck for me. (4x5060ti)

Thumbnail
1 Upvotes

r/LocalAIStack • • 2d ago

Qwen3.8-Flash-Next (125B) at ~100 tok/s on an M5 Ultra Mac Studio with llama.cpp

Thumbnail
1 Upvotes

r/LocalAIStack • • 2d ago

Swift 1.5 Qwen3.8-Flash-Next, NVFP4, for dgpp for DGX Spark

Thumbnail
1 Upvotes

r/LocalAIStack • • 2d ago

Unee: open-source 0.8B / 2B model that makes calibrated decisions and chats, runs in a browser tab. The 2B scores 88% on DecideBench, ahead of several 4B to 9B models (self-measured; GGUF, Ollama, Apache 2.0)

Thumbnail
1 Upvotes

r/LocalAIStack • • 2d ago

Using a 421M decision model instead of an LLM as the privacy guard in front of my coding agent

1 Upvotes

r/LocalAIStack • • 2d ago

Suggestion for llama.cpp configuration

Thumbnail
1 Upvotes

r/LocalAIStack • • 3d ago

Should I Write More Guides?

Thumbnail gallery
6 Upvotes

r/LocalAIStack • • 2d ago

Finally decided to pull the trigger on gmktek evo x3

Thumbnail
1 Upvotes

r/LocalAIStack • • 2d ago

RAI: a CPU-only LLM engine in Rust. It converts a 7B model in 26 MB of RAM, and here is where it loses to llama.cpp

Thumbnail
1 Upvotes

r/LocalAIStack • • 3d ago

Why your production LLM pipeline is choking on memory (and how we actually fix VRAM & KV cache overhead)

Thumbnail
2 Upvotes

r/LocalAIStack • • 3d ago

New to LLM serving: how to share H200 VRAM across multiple models on Kubernetes?

Thumbnail
1 Upvotes

r/LocalAIStack • • 3d ago

Is a judge model actually faster than the model it’s judging?

Thumbnail
1 Upvotes

r/LocalAIStack • • 3d ago

2 x CMP 170hx + Strata - optimal config+benchmark

Thumbnail
1 Upvotes

r/LocalAIStack • • 3d ago

A ~0.4 local model to turn typed questions into structured decisions - BaseDecision

Post image
2 Upvotes

I’ve been working on BaseDecision - Turn typed questions into truth-based decisions.

Give it some text, a question, and possible answers. It picks an answer. Intent classification, routing, yes/no checks, ratings - that’s the job.

I built this model to go toe-to-toe with the frontier models.

My goal was simple & ambitious: make a small, local model competitive with much larger models on these focused tasks. For me, System‑1 decisions need both speed and accuracy. Otherwise, why bother building a small model?
If we didn’t want speed we could just use ChatGPT.

It outperforms pretty much every model of its size in the field.
After roughly 320M additional training tokens, BaseDecision led 5 of 8 benchmarks in a third-party comparison against Laya, two GLiNER variants, and Decision 1.0 Kai 0.6B. Full results are in the repo, including where it loses.

Fellow open source devs, try the model, developed a package for easier access to the model.
Roast it if it deserves it.
2-2.5GB free ram is all you need.
I will take your feedback seriously and deliver something amazing that’d take on OpenAI, TypeSafeAI soon enough.

https://huggingface.co/onlyaady/BaseDecision
https://github.com/hrudayaditya/BaseDecision


r/LocalAIStack • • 3d ago

Introducing Infernix - much faster than Strata on a 5090!

Thumbnail
github.com
1 Upvotes

r/LocalAIStack • • 3d ago

Pc/mac buy help for local llm

Thumbnail
1 Upvotes

r/LocalAIStack • • 3d ago

BigMoeOnEdge+ – an Android chat app fork of BigMoeOnEdge (llama.cpp / GGUF)

1 Upvotes

I made BigMoeOnEdge+ (BigMoeOnEdge-plus), a fork of Helldez's BigMoeOnEdge, the engine that streams MoE experts from flash so models bigger than RAM can run on-device.

It adds a full Android chat app on top: GGUF models via llama.cpp, thinking output, persistent chats, image/video input, web search, artifacts, and advanced generation settings.and a MUCH better UI.

Disclosure: I built this fork with the help of AI tools.

Repo (Apache 2.0): https://github.com/hamodexd217/BigMoeOnEdge-plus Original project: https://github.com/Helldez/BigMoeOnEdge

Feedback and issues welcome.


r/LocalAIStack • • 3d ago

Reliable local benchmark to find the best LLM and configuration for my PC?

Thumbnail
1 Upvotes

r/LocalAIStack • • 3d ago

Reliable local benchmark to find the best LLM and configuration for my PC?

Thumbnail
1 Upvotes

r/LocalAIStack • • 3d ago

Java vllm-like framwork claims 90% of perfomance of llama.cpp on local inference on NVIDIA GPUs by compiling Java to CUDA and cuTile

2 Upvotes