Edge0-145B: Frontier AI Capabilities on Your Own Computer
https://edge0.ai/blog/introducing-edge0-145b-frontier-ai-capabilities-on-your-own-computerTheir GitHub repo and HuggingFace pages directly state that it's based on Qwen3.8 Flash Next. Their inference engine is called edge0-strata because it's a strata fork.
They fine tuned it and tweaked the architecture.
The real question is if the model performs better on general purpose tasks, or if it was just fine tuned to increase the benchmark scores.
145B parameters with only peak 29GB memory usage - really impressive, truly something you can actually run on a decent spec MBP
They're not hiding that fact once you get to the repo. I mean it's called edge0-strata and the README says it's a fork of strata.
It's weird to see this marketing web page imply that it was all new and novel work that they did, though.
ModelTokenMemoryUsage [Bytes] = Parameters * (BitsPerParameter/8)
For a model with 145B parameters, this means:
Precision GB GiB
2-bit 36.25 33.76
3-bit 54.38 50.64
4-bit 72.50 67.52
6-bit 108.75 101.28
8-bit 145.00 135.04
16-bit 290.00 270.08
32-bit 580.00 540.17
It seems that they radically reduce the model information required per token via the sparse expert approach - 145B->10B parameters, probably at lower precision - and then use some other approaches I do not understand at that point to make effective use of loaded information.
Did anyone try the model?
> For Edge0-145B, day-one support covers macOS, Windows, and Linux, including Apple M-series chips and NVIDIA platforms such as RTX Spark, RTX 4090, and RTX 5090. The release includes one-click deployment tools and hardware guides.
But... there's no guidance on how to run on a mac at all. Weird.
At first the work looked awesome, and once I started checking it out, I realized I would have to significantly alter the placements and manually draw most of the schematic to get it up to my standards (and hit the performance criteria of the board).
And I assume the metrics comparing qwen are using a quantized version - from the blog they compare memory use against qwen's "standard 4-bit deployment". Which might explain the improved performance over that base model they show - it might lose less from quantization.
The inference engine they use is called edge0-strata because it's a strata fork.
This project looks like someone fine-tuned Qwen3.8 Flash Next, made some tweaks to the model, forked strata to run it, and then put a lot of marketing and branding on top of it
This looks like it would actually work on my 64mb M1 MBP. That would be fantastic.
I'm curious, do normal people (so not HN professional developers or entrepreneurs) have that, as actually personal device (not from their tech company)?
I imagine some might have but I feel, and that's me so I'm asking the broader community here, that the average PC user has at best a gaming rig, but that's already a minority, and a lot more often a low end laptop with 16GB of normal RAM, not unified or VRAM. Am I out of touch?
It works great, I mainly use 2 applications , Firefox and urxvt, using emacs -nw to write code, no LSP or code completion because they use too much RAM. I also use i3wm which very lightweight.
The only issue with it is that some keys on the keyboard don't work, so I mapped them to different keys, for example ctrl is mapped to caps lock, 0 is mapped to 3 on the numpad, enter is mapped to right shift etc. Because of this I can't type on other peoples computers, I would need to run my xmodmap script before I do that.
It was SO HARD to convince my sister to spend that 100€ more for 16 GB of ram.
My personal laptop is a fairly decent one, still with 16GB of soldered ram.
Just got approved a new laptop for work. 4k for 64GB of RAM and high end CPU. 1.5y ago that would have been 2.5k, only macs would have been that expensive
I guess I'm far from the norm, as I have a Macbook Pro privately. I personally don't have 30+ GB of ram, though I'm sure the next one will have that.
However, I doubt that 'normal people' are even the target market for this. 'Normal people' don't care about their models running in the cloud. They want their voice assistant to be somewhat useful when there's no internet and we don't need 145B models for that.
So I think that I and people like me, often caller prosumers, are the target for these type of things. Some people buy €5k camera lenses for their hobby, others buy €5k laptops.
Those weighting value/price for the use case they are having the Pro is an overkill for most in the population. I have a good but still medium performance laptop personally as I use it for learning experimentation (and fun, which is not independent from the two former), but I did not push such for my wife. For her even the entry level Mac is an overkill. Be both recognise that. For those thinking like us the MacBook Neo was a good move from Apple.
Said that, this was before personal AI assistants. Now, for privacy reasons, I might push for a more capable laptop for her too (if I was sure I could set up a reliable config locally. I did not dig into that enough to make sure, but I'd do if it was an issue today).
It is strange that they also benchmarked an M2 Ultra, but they are specifically not releasing a Mac runtime today. Maybe that is still under development.
- Is there a minimum VRAM requirement? I see the requirement for 29GB RAM (in total, as there are benchmark results with 16GB VRAM) and the requirement for CUDA13, but what is the VRAM requirement? Does it work with 4/6/8GB?
- Where is that company located? Didn't find it in the about us, careers and contact page.
Over the past few weeks, I've been putting together a small, 6.5M param model. It is conversational, does tool calling, RAG, mathematical modeling/calculations, basic physical simulations, and multi-step chained reasoning. It's still very much _not_ a frontier model. But it already does more than I could get a qwen 2.5 0.5b param model to do with finetuning just 1 year ago
Most of the improvements come from being able to direct claude to apply different ideas from recent research that keep coming out. AI labs seem to be focused on 1B+ models, so not much comes out about small models, but many of the things being discovered in research with billion+ model, are adaptable to small ones as well
Been reading this for years now. Seems both, bigger models optimized for scale AND smaller models optimized for single machines, keep on being released. I don't think it's changing, different markets.
This score is roughly on par with 5.6 Terra max, which in my direct experience is perfectly capable of driving a cloud agent harness that can do pretty complex knowledge work.
Definitely worth an evaluation!
I’ll just say this, we obviously all know they (American open weight models) are playing catch-up to the competition. So if your reaction is “why would I use this over Qwen this, or Deepseek that” you’re really missing the mark.
Yeah, they are all going to be behind on benchmarks for now. Nobody realistically expects some American open weight model to come out of nowhere and beat Qwen/ Deepseek/ et cetera. But there has been a flurry of new releases from this segment, and it’s interesting to me to watch how it develops.
How far are we from this kind of throughput with local models?
Also throughput pretty much depends on compute capabilities and active weight. So if someone build a A0.1B then maybe you can look at 1500t/s of pure uselessness.
This weird approach is frustrating, because the actual promise sounds very intriguing, so I'd very much like to test this out.
Based on Qwen3.8-Flash-Next and Strata. They made some interesting tweaks and did some fine tuning on the model, but it's interesting how much marketing speak was layered on their website to hide this fact.
It's not at all unusual for people to fine tune models, tweak them, and fork inference engines. It's highly encouraged and appreciated in the community. What is unusual is seeing someone wrap this all up in extensive branding and publish it online as a company.
As with all fine-tunes, I'd be a little skeptical of the improved benchmark numbers until people actually use it more. It's really easy to take off the shelf models and fine tune them to get slightly higher benchmark scores without really improving them for normal use. HuggingFace is full of fine tunes that advertise amazing results on specific benchmarks but are worse than the base model when you use them for real work.
Whatever happened to "Problem | Solution"
5800X3D + 32 DDR4 GB RAM + AMD 7800 XT 16 GB.
Specially interested in the blender usage.
edit: ah, just read a bit more. seems it will be a waste of my time given the reqs?
Damn, guess I can't run this on my desktop. /s