This open-source engine claims it can run Gemma 4 26B in just 2GB of RAM on M-series Macs — if true, that’s a massive leap in memory efficiency, since a 26B-class model normally eats 10GB+ of RAM without breaking a sweat.
As for token/s numbers or the technical details behind how it pulls off this kind of RAM savings, there’s no solid confirmation yet from verifiable sources. We’ll need to wait for real-world tests from people who’ve run it head-to-head against other engines like llama.cpp or MLX.
If you’re already following on-device LLM work and have an M-series Mac on hand, go ahead and download it to test. But if you need something production-ready or benchmarks that have actually been rigorously measured, it’s better to wait a bit longer for the community to confirm the numbers.
Running a 26B model on 2GB of RAM — what it actually looks like in practice
From what’s in the HN post, the poster says the core trick is streaming weights from disk into RAM piece by piece, instead of loading the whole model into memory at once like typical engines do. That’s what lets it push RAM usage far below normal.
That said, to be blunt: there’s no benchmark data yet that we can verify ourselves. All we’ve seen so far are the claims in the post — no real run logs with concrete numbers to compare against.
If you already own an M-series MacBook and want to try it yourself, the easiest way to check is to open Activity Monitor alongside the run and watch the process’s memory footprint directly. That’ll tell you how closely the claimed numbers match what you actually see.
Say you’ve got an M1 Mac with 8GB of RAM and want to run a large language model locally, so you don’t have to send customer data or company code out to any cloud API.
Once you try loading a 20B+ model directly through common tools, you keep hitting the same wall: not enough RAM, the machine freezes, or it’s too slow to actually use. What’s left is either falling back to a smaller, less capable model, or giving up on the principle and relying on an external API instead.
That’s exactly where this Show HN post grabs attention — it promises running a 26B model in just 2GB of RAM. If it holds up, it solves a bottleneck that low-RAM Mac users have been dealing with for a long time. But the real question is how much of it actually works, and what trade-offs come with it.
Where this engine sits in the local LLM landscape
The on-device inference market right now is dominated by a few main players: llama.cpp (GGUF quantization), MLX (Apple’s own framework, optimized for Apple Silicon), and Ollama, which acts as a wrapper to make things easier to use. This project isn’t trying to replace any of the three — it positions itself as a separate engine focused primarily on memory footprint, unlike MLX, which focuses on speed on Apple Silicon’s unified memory.
Gemma itself is a model family Google designed to be efficient from the ground up (compared to Llama or Mistral at similar sizes), so testing with Gemma first isn’t a coincidence — it’s the model best suited to showing off “how far can you compress RAM usage while staying actually usable.”
How does it compare to previous approaches?
The main difference isn’t in the Gemma model itself, but in the engine running it. The old approach — standard quantization on MLX — still requires unified memory close to the model’s full size. To run something at the 26B level, the machine needs a fairly large amount of free RAM.
The new engine posted on Show HN compresses that down to just 2GB of RAM, which is the number that’s driving most of the discussion — because normally, a model this size simply couldn’t run on a machine with this little memory at all.
As for speed and output quality after this level of compression, the repo hasn’t posted an apples-to-apples benchmark yet. We’ll have to wait for people to actually test it and report back.
| Factor | Old approach (standard quantization on MLX) | New engine (Show HN) |
|---|---|---|
| RAM needed to run 26B | Close to full model size | 2 GB |
| Model file size | Same | Same |
| Speed/quality benchmark | No direct comparison data yet | No direct comparison data yet |
What actually changes in real-world use
The memory-streaming feature — loading only part of the model into RAM instead of the whole thing — is the real game-changer here.
Mapped onto real life, it’s pretty direct: an older MacBook Air that didn’t have enough RAM to run large models before can now open a terminal and run one as a coding assistant, even offline. Great for coding on a plane or anywhere without a signal.
Another use case is a private assistant — data never leaves the machine, running entirely locally. Good fit for anyone concerned about privacy, or working with company data that can’t go out to the cloud.
For anyone with a low-spec backup laptop, this also means running a 26B-class model without buying new hardware. But it’s worth repeating: there’s no direct data yet comparing speed/quality against running with full RAM. We’ll need real test results before drawing conclusions.
What else is out there right now
Compared to the local LLM tools developers already know well, this one’s clear selling point is a noticeably smaller RAM footprint. On speed/quality, there’s no confirmed head-to-head data available yet — we need real benchmarks before drawing conclusions.
| Factor | New engine (2GB RAM) | llama.cpp + standard GGUF | Ollama / MLX |
|---|---|---|---|
| RAM required (comparable model) | Much lower (claimed) | Higher | Higher |
| Ease of install/use | New, no large community yet | Lots of docs, easy to find guides | Easiest — GUI/CLI ready to go |
| Community/maintenance | Just launched, still small | Large, battle-tested for years | Large, backed by Apple ecosystem |
| M-series Mac only | Yes | No, cross-platform | MLX is Apple-only / Ollama is cross-platform |
If stability is your priority right now, Ollama or llama.cpp are still the safer bet. But if you want to try something new that sips RAM, this engine is worth keeping an eye on.
Pros and cons worth knowing before you try it
This project is interesting because it addresses a real pain point for people running older, RAM-limited Macs. But before diving in, it’s worth weighing both the strengths and the weaknesses.
Pros
- +Real RAM savings — running a 26B model in just 2GB opens the door for older or lower-spec Macs to access large LLMs
- +Open source — you can modify or extend the code yourself, with no single-vendor lock-in
- +Runs on older hardware — no need to buy a new powerful machine just to try LLMs
Cons
- −Output quality may drop due to heavy compression used to save RAM — some accuracy is likely traded away
- −The project is still very new; stability isn't as solid as tools that have been around for years
- −Speed may be slower than expected, since low RAM usage is traded for heavier compute
- −Documentation is still sparse — setup/debugging may require digging through the source code yourself
Bottom line: good for people who want to experiment with something new, not for production work that needs stability.
The cost that isn’t on the 2GB number
The 2GB figure looks great on the README, but the real cost isn’t there. Time spent on setup, reading source code instead of documentation — that’s the hidden cost that doesn’t show up in any benchmark chart.
Running a model this compressed continuously for a long stretch will make your Mac’s fan work harder and the machine run hotter. On a MacBook, that also means faster battery drain than normal usage, though the exact numbers will vary by model.
Another thing worth thinking through: how exactly do you compress a model down to fit in 2GB of RAM? The answer, in most cases, is quantization — which usually comes with some trade-off in answer quality. And the biggest risk of all: this is a brand-new open-source project. If the maintainer stops working on it, you’re on your own for migrating away.
Who it’s right for, and who should wait
Made for
- Developers with low-RAM Macs who want to run LLMs locally without relying on cloud APIs
- Privacy-focused users who don't want data leaving the machine and can tolerate an early-stage project's rough edges
- Open-source tinkerers who like trying new things early and aren't afraid of bugs
Think twice
- Teams planning to build this into a real product — budget time to test how much quantization affects answer quality
Skip this one
- Anyone who needs production-grade stability today — better to wait for the maintainer to stabilize things further
- Anyone not comfortable with the command line or building from source — this kind of project doesn't have an easy-to-use GUI yet
This trend tells us one thing clearly: the wall between “big models” and “small machines” keeps getting thinner.
It used to take a powerful GPU or tens of gigabytes of RAM to run a 26B model. Today, that’s down to 2GB on an ordinary MacBook — and if engines like this keep improving, the next question is how close post-quantization speed and accuracy can get to the full model.
For anyone who wants to follow this space, my advice is to start by trying it on hardware you already own — no need to buy anything new. Check the GitHub repo to see how far the community has gotten on fixing context and quantization bugs, and watch whether major frameworks like llama.cpp or MLX end up absorbing this idea into the mainstream. That’s the moment local AI stops being a niche curiosity and becomes something people actually use every day.