zai-org/GLM-4.5-Air · Hugging Face
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GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter size. GLM-4.5-Air also supports hybrid inference modes, offering a "thinking mode" for advanced reasoning and tool use, and a "non-thinking mode" for real-time interaction. Users can control the reasoning behaviour with the reasoning enabled boolean. Learn more in our docs
Blog post: https://z.ai/blog/glm-4.5
Hugging Face:
I'm currently using ollama to serve llms, what's everyone using for these models?
I'm also using open webui as well and ollama seemed the easiest (at the time) to use in conjunction with that
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I'm currently using ollama to serve llms, what's everyone using for these models?
I'm also using open webui as well and ollama seemed the easiest (at the time) to use in conjunction with that
wrote last edited by [email protected]ik_llama.cpp (and its API server) is the end-all stop for these big MoE models. Level1tech just did a video on it, and check out ubergarm's quants on huggingface: https://huggingface.co/ubergarm
TabbyAPI (exllamav3 underneath) is great for dense models, or MoEs that will just barely squeeze onto your GPU at 3bpw. Look for exl3s: https://huggingface.co/models?sort=modified&search=exl3
Both are massively more efficient than ollama defaults, to the extent you can run models at least twice the equivalent parameter count ollama can, and support more features too. ik_llama.cpp is also how folks are running these 300B+ MoEs on a single 3090/4090 (in conjunction with a Threadripper, Xeon or EPYC, usually).
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I'm currently using ollama to serve llms, what's everyone using for these models?
I'm also using open webui as well and ollama seemed the easiest (at the time) to use in conjunction with that
I use Kobold most of the time.
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I'm currently using ollama to serve llms, what's everyone using for these models?
I'm also using open webui as well and ollama seemed the easiest (at the time) to use in conjunction with that
I've moved to using Rama Lama mainly because it promises to do the probing to get the best acceleration possible for whatever model you launch.
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I've moved to using Rama Lama mainly because it promises to do the probing to get the best acceleration possible for whatever model you launch.
wrote last edited by [email protected]It looks like it just chooses a llama.cpp backend to compile, so technically you are leaving a good bit of performance/model size on the table if you know your GPU, and the backend to choose.
All this stuff is horribly documented though.
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ik_llama.cpp (and its API server) is the end-all stop for these big MoE models. Level1tech just did a video on it, and check out ubergarm's quants on huggingface: https://huggingface.co/ubergarm
TabbyAPI (exllamav3 underneath) is great for dense models, or MoEs that will just barely squeeze onto your GPU at 3bpw. Look for exl3s: https://huggingface.co/models?sort=modified&search=exl3
Both are massively more efficient than ollama defaults, to the extent you can run models at least twice the equivalent parameter count ollama can, and support more features too. ik_llama.cpp is also how folks are running these 300B+ MoEs on a single 3090/4090 (in conjunction with a Threadripper, Xeon or EPYC, usually).
Thanks, will check that out!
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Thanks, will check that out!
Just read the L1 post and I'm just now realizing this is mainly for running quants which I generally avoid
I guess I could spin it up just to mess around with it but probably wouldn't replace my main model
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Just read the L1 post and I'm just now realizing this is mainly for running quants which I generally avoid
I guess I could spin it up just to mess around with it but probably wouldn't replace my main model
wrote last edited by [email protected]Just read the L1 post and I’m just now realizing this is mainly for running quants which I generally avoid
ik_llama.cpp supports special quantization formats incompatible with mainline llama.cpp. You can get better performance out of them than regular GGUFs.
That being said... are you implying you run LLMs in FP16? If you're on a huge GPU (or running a small model fast), you should be running sglang or vllm instead, not llama.cpp (which is basically designed for quantization and non-enterprise hardware), especially if you are making parallel calls.
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Just read the L1 post and I’m just now realizing this is mainly for running quants which I generally avoid
ik_llama.cpp supports special quantization formats incompatible with mainline llama.cpp. You can get better performance out of them than regular GGUFs.
That being said... are you implying you run LLMs in FP16? If you're on a huge GPU (or running a small model fast), you should be running sglang or vllm instead, not llama.cpp (which is basically designed for quantization and non-enterprise hardware), especially if you are making parallel calls.
yeah, im currently running the gemma 27b model locally
I recently took a look at vllm but the only reason i didnt want to switch is because it doesnt have automatic offloading (seems that it's a manual thing right now) -
yeah, im currently running the gemma 27b model locally
I recently took a look at vllm but the only reason i didnt want to switch is because it doesnt have automatic offloading (seems that it's a manual thing right now)wrote last edited by [email protected]Gemma3 in particular has basically no reason to run unquantized since Google did a QAT (quantization aware training) finetune of it. The Q4_0 is almost, objectively, indistinguishable from the BF16 weights. Llama.cpp also handles its SWA (sliding window attention) well (whereas last I checked vllm does not).
vllm does not support CPU offloading well like llama.cpp does.
...Are you running FP16 models offloaded?
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Gemma3 in particular has basically no reason to run unquantized since Google did a QAT (quantization aware training) finetune of it. The Q4_0 is almost, objectively, indistinguishable from the BF16 weights. Llama.cpp also handles its SWA (sliding window attention) well (whereas last I checked vllm does not).
vllm does not support CPU offloading well like llama.cpp does.
...Are you running FP16 models offloaded?
omg, I'm retarded. Your comment made me start thinking about things and...I've been using q4 without knowing it... I assumed ollama ran the fp16 by default
about vllm, yeah I see that you have to specify how much to offload manually which I wasn't a fan of. I have 4x 3090 in an ML server at the moment but I'm using those for all AI workloads so the VRAM is shared for TTS/STT/LLM/Image Gen
thats basically why I kind of really want auto offload
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omg, I'm retarded. Your comment made me start thinking about things and...I've been using q4 without knowing it... I assumed ollama ran the fp16 by default
about vllm, yeah I see that you have to specify how much to offload manually which I wasn't a fan of. I have 4x 3090 in an ML server at the moment but I'm using those for all AI workloads so the VRAM is shared for TTS/STT/LLM/Image Gen
thats basically why I kind of really want auto offload
wrote last edited by [email protected]Oh jeez. Do they have nvlink?
Also I don’t know if ollama even defaults to QAT heh. It has a lot of technical issues.
My friend, you need to set up something different. If you want pure speed, run vllm and split Gemma evenly across 3 or four of those 3090s, and manually limit its vram to like 30% each or whatever it will take. Vllm will take advantage of nvlink for splitting and make it extremely fast. Use an AWQ made from Gemma QAT.
But you can also run much better models than that. I tend to run Nemotron 49B on a single 3090 via TabbyAPI, for instance, but you can run huge models with tons of room to spare for other ML stuff. You could probably run a huge MoE like Deepseek on that rig, depending on its RAM capacity. Some frameworks like TabbyAPI can hot swap them too, as different models have different strengths.
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Oh jeez. Do they have nvlink?
Also I don’t know if ollama even defaults to QAT heh. It has a lot of technical issues.
My friend, you need to set up something different. If you want pure speed, run vllm and split Gemma evenly across 3 or four of those 3090s, and manually limit its vram to like 30% each or whatever it will take. Vllm will take advantage of nvlink for splitting and make it extremely fast. Use an AWQ made from Gemma QAT.
But you can also run much better models than that. I tend to run Nemotron 49B on a single 3090 via TabbyAPI, for instance, but you can run huge models with tons of room to spare for other ML stuff. You could probably run a huge MoE like Deepseek on that rig, depending on its RAM capacity. Some frameworks like TabbyAPI can hot swap them too, as different models have different strengths.
Unfortunately i didn't set up nvlink, but ollama auto splits things for models which require it
I really just a "set and forget" model server lol (that's why I keep mentioning the auto offload)
Ollama integrates nicely with OWUI
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Unfortunately i didn't set up nvlink, but ollama auto splits things for models which require it
I really just a "set and forget" model server lol (that's why I keep mentioning the auto offload)
Ollama integrates nicely with OWUI
wrote last edited by [email protected]Basically any backend will integrate with open web ui since they all support OpenAI API. In fact, some will support more sampling, embeddings models for RAG and such.
They will mostly all do auto split too. TabbyAPI (which I would specifically recommend if you don’t have nvlink set up) is completely automated splitting across GPUs, vllm is completely automated. IK_llama.cpp (for the absolute biggest models) needs a more specific launch command, but there are good guides for it.
IMO it’s worth it to drill down some making one “set it and forget it” config, as these 200B MoEs that keep coming out will blow ollama Gemma 27B away, depending on what you use it for.
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Basically any backend will integrate with open web ui since they all support OpenAI API. In fact, some will support more sampling, embeddings models for RAG and such.
They will mostly all do auto split too. TabbyAPI (which I would specifically recommend if you don’t have nvlink set up) is completely automated splitting across GPUs, vllm is completely automated. IK_llama.cpp (for the absolute biggest models) needs a more specific launch command, but there are good guides for it.
IMO it’s worth it to drill down some making one “set it and forget it” config, as these 200B MoEs that keep coming out will blow ollama Gemma 27B away, depending on what you use it for.
I'll take a look at both tabby and vllm tomorrow
Hopefully there's cpu offload in the works so I can test those crazy models without too much fiddling in the future (server also has 128gb of ram)
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I'll take a look at both tabby and vllm tomorrow
Hopefully there's cpu offload in the works so I can test those crazy models without too much fiddling in the future (server also has 128gb of ram)
wrote last edited by [email protected]If you want CPU offload, IK_llama.cpp is explicitly designed for that and your go-to. It keeps the “dense” part of the model on the GPUs and offloads the lightweight MoE bits to CPU
Vllm and exllama are GPU only. Vllm's niche is that it’s very fast with short context parallel calls (aka for serving dozens of users at once with small models), while exllama uses SOTA quantization for squeezing large models onto GPUs with minimal loss.
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I'll take a look at both tabby and vllm tomorrow
Hopefully there's cpu offload in the works so I can test those crazy models without too much fiddling in the future (server also has 128gb of ram)
Oh, and I forgot, check out ubergarm's quants:
https://huggingface.co/ubergarm
They’re explicitly designed for RAM offloading.
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If you want CPU offload, IK_llama.cpp is explicitly designed for that and your go-to. It keeps the “dense” part of the model on the GPUs and offloads the lightweight MoE bits to CPU
Vllm and exllama are GPU only. Vllm's niche is that it’s very fast with short context parallel calls (aka for serving dozens of users at once with small models), while exllama uses SOTA quantization for squeezing large models onto GPUs with minimal loss.
IK sounds promising!
Will check it out to see if it can run in a container -
IK sounds promising!
Will check it out to see if it can run in a containerI'm just gonna try vllm, seems like ik_llama.cpp doesnt have a quick docker method
