I have written gemma3 inference in pure C | Hacker News Reader

I have written gemma3 inference in pure C

github.com

65 pointsrobitec9723 comments

My first implementation of gemma.cpp was kind of like this.

There's such a massive performance differential vs. SIMD though that I learned to appreciate SIMD (via highway) as one sweet spot of low-dependency portability that sits between C loops and the messy world of GPUs + their fat tree of dependencies.

If anyone want to learn the basics - whip out your favorite LLM pair programmer and ask it to help you study the kernels in the ops/ library of gemma.cpp:

https://github.com/google/gemma.cpp/tree/main/ops

:D Your code was nicely written and it was a pleasure to port to SIMD because it was already very data-parallel.

I'm really charmed by this project (I know there are a few like it).

In particular it's got a single ~600 line file (https://github.com/robitec97/gemma3.c/blob/main/gemma3_kerne...) with a clear straightforward implementation of every major function used in inferencing (google's models) from gelu to rope.

I'm curious how many more functions you'd need to add to have full coverage of every publically available LLM innovation (e.g. QK-Norm from Qwen3, SwiGLU etc.).

Obviously llama.cpp has a much bigger library but it's lovely to see everything in one clean file.

> It proves that modern LLMs can run without Python, PyTorch, or GPUs.

Did we need any proof of that ?

Python and PyTorch all call out to C libraries… I don’t get what he means by “proving LLMs can run without Python and PyTorch” at all. Seems like they don’t understand basic fundamentals about things here…

I guess llama.cpp isn't quite as popular as I had assumed.

llama.cpp being the best choice doesn't make it popular.

When I got started, I was led to ollama and other local-llm freemium.

I didn't necessarily assume that they weren't c++(I don't even know) but I do think that –as implied– Python duct-tape solutions are more popular than llama.cpp.

A bizarre claim like that would be what happens when you let an LLM write the README without reading it first.

Knowing the performance is interesting. Apparently it's 1-3 tokens/second.

ikllama.cpp is a fork of llama.cpp which specializes on CPU inference, some benchmarks from 1 year ago: https://github.com/ikawrakow/ik_llama.cpp/discussions/164

I imagine so regarding GPUs, right? Is this is a legitimate project then doesn’t it provide a proof of concept for performance constraints that relate to them? Couldn't the environmentally concerned take this as an indicator that the technology can progress without relying on as much energy is potentially spent now? Shouldn’t researchers in the industry be thinking of ways to prevent the future capabilities of the technology from outrunning the capacity of the infrastructure?

I know very little about AI but these are things that come to mind here for me.

GPUs are more efficient than CPUs for LLM inference, using less energy per token and being cheaper overall. Yes, a single data center GPU draws a lot of power and costs a fortune, but it can also serve a lot more people in the time your CPU or consumer GPU needs to respond to a single prompt.

I got you, thanks!

Anyone using this model for something useful? For now I only have use cases for top performing models...

but why tho? next gemma is coming and no one uses gemma 3 in prod anyway.

I think /* */ single-line comments is a pretty good indication.

> no one uses gemma 3 in prod anyway.

Umm, we do. It's still one of the best for eu countries support / help chatbot style. It's got good (best?) multilingual support ootb, it's very "safe" (won't swear, won't display chinese characters, etc) and it's pretty fast.

Yep. Before gemma3 we where struggling with multilinguality on smaller European languages, and it is still one of the batter ones in that regard (even large open or closed models struggle with this to some extent). Gemma3 also is still pretty decent multi modal wise.

I didn't know this was a thing until I read this thread but I can confirm that it does fine(not perfect by any means just like the average casual non-native fluent speaker) and it is one of the reasons I use it as my local model.

but it lacks system prompt support.

It lacks a deducated system prompt, but it was trained with and in practice works with the system prompt be the first message from the user.

I don't have firsthand knowledge, but r/SesameAI seems to believe Maya/Miles products are based on a Gemma3 backbone.

Gemma3 is probably the best supported fine tunable model.

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