Great, I've been experimenting with OpenCode and running local 30B-A3B models on llama.cpp (4 bit) on a 32 GB GPU so there's plenty of VRAM left for 128k context. So far Qwen3-coder gives the me best results. Nemotron 3 Nano is supposed to benchmark better but it doesn't really show for the kind of work I throw at it, mostly "write tests for this and that method which are not covered yet". Will give this a try once someone has quantized it in ~4 bit GGUF.
Codex is notably higher quality but also has me waiting forever. Hopefully these small models get better and better, not just at benchmarks.
> Codex is notably higher quality but also has me waiting forever.
And while it usually leads to higher quality output, sometimes it doesn't, and I'm left with a bs AI slop that would have taken Opus just a couple of minutes to generate anyway.
I find it hard to trust post training quantizations. Why don't they run benchmarks to see the degradation in performance? It sketches me out because it should be the easiest thing to automatically run a suite of benchmarks
One thing to consider is that this version is a new architecture, so it’ll take time for Llama CPP to get updated. Similar to how it was with Qwen Next.
There are a bunch of 4bit quants in the GGUF link and the 0xSero has some smaller stuff too. Might still be too big and you'll need to ungpu poor yourself.
Looks like solid incremental improvements. The UI oneshot demos are a big improvement over 4.6. Open models continue to lag roughly a year on benchmarks; pretty exciting over the long term. As always, GLM is really big - 355B parameters with 31B active, so it’s a tough one to self-host. It’s a good candidate for a cerebras endpoint in my mind - getting sonnet 4.x (x<5) quality with ultra low latency seems appealing.
I tried Cerebras with GLM-4.7 (not Flash) yesterday using paid API credits ($10). They have rate limits per-minute and it counts cached tokens against it so you'll get limited in the first few seconds of every minute, then you have to wait the rest of the minute. So they're "fast" at 1000 tok/sec - but not really for practical usage. You effectively get <50 tok/sec with rate limits and being penalized for cached tokens.
They also charge full price for the same cached tokens on every request/response, so I burned through $4 for 1 relatively simple coding task - would've cost <$0.50 using GPT-5.2-Codex or any other model besides Opus and maybe Sonnet that supports caching. And it would've been much faster.
Note that this is the Flash variant, which is only 31B parameters in total.
And yet, in terms of coding performance (at least as measured by SWE-Bench Verified), it seems to be roughly on par with o3/GPT-5 mini, which would be pretty impressive if it translated to real-world usage, for something you can realistically run at home.
We’ve launched GLM-4.7-Flash, a lightweight and efficient model designed as the free-tier version of GLM-4.7, delivering strong performance across coding, reasoning, and generative tasks with low latency and high throughput.
The update brings competitive coding capabilities at its scale, offering best-in-class general abilities in writing, translation, long-form content, role play, and aesthetic outputs for high-frequency and real-time use cases.
For anyone who’s already running this locally: what’s the simplest setup right now (tooling + quant format)? If you have a working command, would love to see it.
Interesting they are releasing a tiny (30B) variant, unlike the 4.5-air distill which was 106B parameters. It must be competing with gpt mini and nano models, which personally I have found to be pretty weak. But this could be perfect for local LLM use cases.
In my ime small tier models are good for simple tasks like translation and trivia answering, but are useless for anything more complex. 70B class and above is where models really start to shine.
Every time I've tried to actually use gpt-oss 20b it's just gotten stuck in weird feedback loops reminiscent of the time when HAL got shut down back in the year 2001. And these are very simple tests e.g. I try and get it to check today's date from the time tool to get more recent search results from the arxiv tool.
It actually seems worse. gpt-20b is only 11 GB because it is prequantized in mxfp4. GLM-4.7-Flash is 62 GB. In that sense GLM is closer to and actually is slightly larger than gpt-120b which is 59 GB.
Also, according to the gpt-oss model card 20b is 60.7 (GLM claims they got 34 for that model) and 120b is 62.7 on SWE-Bench Verified vs GLM reports 59.7
GLM itself is quite inexpensive. A year sub to their coding plan is only $29 and works with a bunch of various tools. I use it heavily as a "I don't want to spend my anthropic credits" day-to-day model (mostly using Crush)
We don't have lot of GPUs available right now, but it is not crazy hard to get it running on our MI300x. Depending on your quant, you probably want a 4x.
ssh admin.hotaisle.app
Yes, this should be made easier to just get a VM with it pre-installed. Working on that.
Great, I've been experimenting with OpenCode and running local 30B-A3B models on llama.cpp (4 bit) on a 32 GB GPU so there's plenty of VRAM left for 128k context. So far Qwen3-coder gives the me best results. Nemotron 3 Nano is supposed to benchmark better but it doesn't really show for the kind of work I throw at it, mostly "write tests for this and that method which are not covered yet". Will give this a try once someone has quantized it in ~4 bit GGUF.
Codex is notably higher quality but also has me waiting forever. Hopefully these small models get better and better, not just at benchmarks.
> Codex is notably higher quality but also has me waiting forever.
And while it usually leads to higher quality output, sometimes it doesn't, and I'm left with a bs AI slop that would have taken Opus just a couple of minutes to generate anyway.
https://huggingface.co/unsloth/GLM-4.7-GGUF
This user has also done a bunch of good quants:
https://huggingface.co/0xSero
I find it hard to trust post training quantizations. Why don't they run benchmarks to see the degradation in performance? It sketches me out because it should be the easiest thing to automatically run a suite of benchmarks
Yes I usually run Unsloth models, however you are linking to the big model now (355B-A32B), which I can't run on my consumer hardware.
The flash model in this thread is more than 10x smaller (30B).
When the Unsloth quant of the flash model does appear, it should show up as unsloth/... on this page:
https://huggingface.co/models?other=base_model:quantized:zai...
Probably as:
https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF
it'a a new architecture. Not yet implemented in llama.cpp
issue to follow: https://github.com/ggml-org/llama.cpp/issues/18931
One thing to consider is that this version is a new architecture, so it’ll take time for Llama CPP to get updated. Similar to how it was with Qwen Next.
There are a bunch of 4bit quants in the GGUF link and the 0xSero has some smaller stuff too. Might still be too big and you'll need to ungpu poor yourself.
yeah there is no way to run 4.7 on a 32g vram this flash is something that im also waiting to try later tonight
Looks like solid incremental improvements. The UI oneshot demos are a big improvement over 4.6. Open models continue to lag roughly a year on benchmarks; pretty exciting over the long term. As always, GLM is really big - 355B parameters with 31B active, so it’s a tough one to self-host. It’s a good candidate for a cerebras endpoint in my mind - getting sonnet 4.x (x<5) quality with ultra low latency seems appealing.
Unless one of the open model labs has a breakthrough, they will always lag. Their main trick is distilling the SOTA models.
People talk about these models like they are "catching up", they don't see that they are just trailers hooked up to a truck, pulling them along.
I tried Cerebras with GLM-4.7 (not Flash) yesterday using paid API credits ($10). They have rate limits per-minute and it counts cached tokens against it so you'll get limited in the first few seconds of every minute, then you have to wait the rest of the minute. So they're "fast" at 1000 tok/sec - but not really for practical usage. You effectively get <50 tok/sec with rate limits and being penalized for cached tokens.
They also charge full price for the same cached tokens on every request/response, so I burned through $4 for 1 relatively simple coding task - would've cost <$0.50 using GPT-5.2-Codex or any other model besides Opus and maybe Sonnet that supports caching. And it would've been much faster.
I hope cerebras figures out a way to be worth the premium - seeing two pages of written content output in the literal blink of an eye is magical.
> The UI oneshot demos are a big improvement over 4.6.
This is a terrible "test" of model quality. All these models fail when your UI is out of distribution; Codex gets close but still fails.
Sonnet was already very good a year ago, do open weights model right are as good ?
Fwiw Sonnet 4.5 is very far ahead of where sonnet was a year ago
Note that this is the Flash variant, which is only 31B parameters in total.
And yet, in terms of coding performance (at least as measured by SWE-Bench Verified), it seems to be roughly on par with o3/GPT-5 mini, which would be pretty impressive if it translated to real-world usage, for something you can realistically run at home.
This is their blurb about the release:
https://docs.z.ai/release-notes/new-released> SWE-bench Verified 59.2
This seems pretty darn good for a 30B model. That's significantly better than the full Qwen3-Coder 480B model at 55.4.
When I want fast I reach for Gemini, or Cerebras: https://www.cerebras.ai/blog/glm-4-7
GLM 4.7 is good enough to be a daily driver but it does frustrate me at times with poor instruction following.
What’s the significance of this for someone out of the loop?
You can run gpt 5 mini level ai on your MacBook with 32 gb ram.
You can get LLM as a service for cheaper.
E.g. This model costs less than a tenth of Haiku 4.5.
For anyone who’s already running this locally: what’s the simplest setup right now (tooling + quant format)? If you have a working command, would love to see it.
I would look into running a 4 bit quant using llama cpp (or any of its wrappers)
I'm glad they're still releasing models dispite going public
Interesting they are releasing a tiny (30B) variant, unlike the 4.5-air distill which was 106B parameters. It must be competing with gpt mini and nano models, which personally I have found to be pretty weak. But this could be perfect for local LLM use cases.
In my ime small tier models are good for simple tasks like translation and trivia answering, but are useless for anything more complex. 70B class and above is where models really start to shine.
Not much info than being a 31B model. Here's info on GLM-4.7[0] in general.
I suppose Flash is merely a distillation of that. Filed under mildly interesting for now.
[0] https://z.ai/blog/glm-4.7
How interesting it is depends purely on your use-case. For me this is the perfect size for running fine-tuning experiments.
A3.9B MoE apparently
I'm trying to run it, but getting odd errors. Has anybody managed to run it locally and can share the command?
Excited to test this out. We need a SOTA 8B model bad though!
https://docs.mistral.ai/models/ministral-3-8b-25-12
Is essentialai/rnj-1 not the latest attempt at that?
https://huggingface.co/EssentialAI/rnj-1
Seems to be marginally better than gpt-20b, but this is 30b?
I find gpt-oss 20b very benchmaxxed and as soon as a solution isn't clear it will hallucinate.
Every time I've tried to actually use gpt-oss 20b it's just gotten stuck in weird feedback loops reminiscent of the time when HAL got shut down back in the year 2001. And these are very simple tests e.g. I try and get it to check today's date from the time tool to get more recent search results from the arxiv tool.
It actually seems worse. gpt-20b is only 11 GB because it is prequantized in mxfp4. GLM-4.7-Flash is 62 GB. In that sense GLM is closer to and actually is slightly larger than gpt-120b which is 59 GB.
Also, according to the gpt-oss model card 20b is 60.7 (GLM claims they got 34 for that model) and 120b is 62.7 on SWE-Bench Verified vs GLM reports 59.7
Any cloud vendor offering this model? I would like to try it.
z.ai itself, or Novita fow now, but others will follow soon probably
https://openrouter.ai/z-ai/glm-4.7-flash/providers
Interesting, it costs less than a tenth than Haiku.
GLM itself is quite inexpensive. A year sub to their coding plan is only $29 and works with a bunch of various tools. I use it heavily as a "I don't want to spend my anthropic credits" day-to-day model (mostly using Crush)
https://huggingface.co/inference/models?model=zai-org%2FGLM-... :)
We don't have lot of GPUs available right now, but it is not crazy hard to get it running on our MI300x. Depending on your quant, you probably want a 4x.
ssh admin.hotaisle.app
Yes, this should be made easier to just get a VM with it pre-installed. Working on that.
Unless using docker, if vllm is not provided and built against ROCm dependencies it’s going to be time consuming.
It took me quite some time to figure the magic combination of versions and commits, and to build each dependency successfully to run on an MI325x.
Agreed, the OOB experience kind of suck.
Here is the magic (assuming a 4x)...
The model literally came out less than a couple hours ago, it's going to take people a while in order to tool it for their inference platforms.
Sometimes model developers coordinate with inference platforms to time releases in sync.