What I most want to see it compared to is Gemma 4 12B in the 4-bit QAT version. It's barely bigger than this at just under 7GB, so it also runs on just about any modern device and is remarkably smart for its size. It's an excellent tool user, crazy good vision for its size. I'm still trying to wrap my head around how much is lost with each step down in resolution, but the QAT versions from Google seem to prove the answer is "very little" at four bits.

I need help understanding this. I understood that the magic here is the quantization that allows it to use from 50G to 4G and their process retain most of the intelligence within Pareto limits of gain. And then they proceed to compare with other quantized models as in the level of intelligence per size. It gets to my attention though that the performance in tool calling is mostly affected which is a problem for other small models.

How does this model compare to a recent 4G model? How do we know it retained intelligence from the parent rather then being fine tuned for the benchmarks?

I am not shtng on them or anything. I'd rather find it amazing, BUT given my limited knowledge, I feel the results miss fair comparison plots and the ones might be misleading. Buy I also reckon it might be me the problem. Anyone care to explain this poor silly fellow some of those points?

Apparently Apple is "in talks" with the PrismML: https://www.cnbc.com/2026/07/14/apple-prismml-ai-compression...

Awesome! I've been waiting for them to start scaling ternary models for over a year[1]. Excited to try it out, typical Qwen 27B is too heavy for me to run on my local hardware at reasonable speeds.

[1] https://jackson.dev/post/dont-sleep-on-bitnet/

This is going in a good direction.

The models themselves are showing up on Hugging Face here: https://huggingface.co/prism-ml/models

I've tried a couple in LM Studio - the GGUF one and the MLX one - but neither worked there. Anyone else get them to work? Might be that LM Studio needs to upgrade their llama.cpp or MLX engines first.

Quite weird that heavy quantization method on a dense model gives better results than slightly quantized MoE models like 35B-A3B from Google.

At this point all the different quantization and 'compression' (look at MPO applied to LLMs...) techniques start feeling a bit like snake oil. It's just gut feeling - or scores on benchmarks models are optimized for - what ends up deciding whether a technique is good enough or not.

The KV-cache memory usage also seems remarkably frugal, even at the full context length. That could make this model particularly useful in multi-agent coding workflows.

I wish KV-cache memory usage and related optimizations were discussed more clearly in new model announcements and demos.

After using a highly capable 2-bit quant as my daily driver for months now, I get pretty excited about releases like this. After a few days for the kinks to be worked out, I’ll be excited to try it.

What's the hiring space and business strategy around all of these smaller AI labs? Its really cool that people like these guys get paid to optimize models and give them out for free (open source). Do a lot of these labs have forward deployed engineers doing integrations with customers who want local models? Is there a general shift towards the local model crowd?

Preliminary analysis via lm-evaluation-harness + vllm

    model         | wikitext | gsm8k (match/error)|
    baseline      | 8.00     | 0.50/0.09 |
    nvfp4-gptq    | 8.25     | 0.47/0.9 |
    nvfp4a16-gptq | 8.11     | 0.53/0.9 |
    bonsai-4bit   | 16.75    | 0/0      |

Looks like they quant'd too hard at 4 bits, can't imagine the ternary being any good based on this.

Code if you'd like to reproduce or try other test sets: https://github.com/verdverm/quantr (lightly tuned to a single oem spark, probably possible in 32-48G)

Good paper to understand the effects of quant regimes across model families and tasks: https://arxiv.org/abs/2402.18158 (Evaluating Quantized Large Language Models - 2024 ICML)

TIL that 1 bit models are actually 1.58 bit with three values +1, 0 and -1

The problem, of course, is if you run the UD_Q2 variant (Unsloth) which does only post-training, the number is pretty close to 1-bit model here and the 5% drop in tool-call is significant than it suggests in real-life use cases.

Tried it on Android and got "!!!!!!!!!!!!!" for answers.

For those curious about their demo, I’m pretty sure it’s using Locally AI (iOS only) that lmstudio acquired/aquihired a couple months ago.

I've been watching and waiting for this, interested to see how smart it is, as it fits with my interest of getting the smartest possible model running in 10GB of VRAM (RTX3060 that has to drive 2 monitors and run an llm)

I don’t know if the llama cpp implementation is wonky (and only supports the binary version) but it’s a lot slower than 35B-A3B @ Q4_KM + MTP with CPU offloading.

Entire blog post seems to be AI-generated :/

27B is way more than you need for a phone. Doesn't matter how much you try to compress it, it's the wrong application of the wrong tool. There are already useful tiny models that fit on phones and do basic things really well. Dumb down a big model too much and it becomes worse than a small fine-tuned model.

That's awesome. What's the largest model that could fit onto a single 16gb gpu at 1.125 effects bits per weight?

I was trying Ornith 9B locally (it's up on Ollama) which claims:

> Ornith-1.0-9B, which can be easily deployed on edge devices, matches or exceeds the performance of much larger models such as Gemma 4-31B and Qwen 3.6 35B.

https://deep-reinforce.com/ornith_1_0.html

Only tried it so much so far; it did a little better than Qwen 9B

This must be some sort of unpublished app?

I can just see their image tool on the app store

This is useful research, but this particular model itself is likely absolutely useless.