Instructions to use zai-org/GLM-5.3-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/GLM-5.3-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/GLM-5.3-Flash") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("zai-org/GLM-5.3-Flash") model = AutoModelForMultimodalLM.from_pretrained("zai-org/GLM-5.3-Flash", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zai-org/GLM-5.3-Flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zai-org/GLM-5.3-Flash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5.3-Flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zai-org/GLM-5.3-Flash
- SGLang
How to use zai-org/GLM-5.3-Flash with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "zai-org/GLM-5.3-Flash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5.3-Flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "zai-org/GLM-5.3-Flash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5.3-Flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use zai-org/GLM-5.3-Flash with Docker Model Runner:
docker model run hf.co/zai-org/GLM-5.3-Flash
How is this "Flash"?
This is full size of GLM-4.7 or half of 5.2. Calling it "flash" is a very weird marketing ploy. It is nonsensical with your own naming convention (even calling it "Air" would be a stretch). It is not for consumer hardware. Don't advertise it as such. Call it GLM-5.3-Turbo or something (similar to how DiT models come in Base and Turbo versions that are usually 2x smaller).
People with consumer hardware waited for a GLM-4.7-Flash successor, not for this.
I think the naming is a bit confusing too. GLM-4.7-FLASH is a 30B-A3B model, while GLM-4.5-AIR or GLM-4.6V is a 106B-A12B model. That's way too big to be called "flash"
If you want to call it GLM-5.3-AIR, that wouldn't be wrong either, because 106B is roughly one-third of 352B, and 321B is also about one-third of 744B - so we can all just consider it one-third for now.
Yeah, not a bad flash =))) it’s like Mistral small 4=)))
Deepseek 284B is also called 'flash', chill out.
Deepseek-V4-Flash is 6 times smaller than V4-Pro. For GLM that would be 120B, not 320B.
Yes! We need a true successor to the GLM 4.7 Flash, not a fake "Flash" model that's 10x bigger and impossible to run on local hardware.
It's even 3x bigger than the Air! How are we supposed to run it when Flash was supposed to be a fast model for GPU poor local hardware?
Sorry Z.AI, but we don't have 300GB of VRAM at home. At least the new Qwen Flash is 3x smaller, you can offload it to RAM and it'll still run at a decent speed because of engrams that GLM also doesn't have...
So now a model doesn’t deserve to be called “Flash” unless it can run on consumer hardware? Lmao.
The funniest part is that the entire meltdown from people like you comes from your little fantasy not becoming reality: you want ultra-low VRAM usage AND Opus 4.8-tier performance at the same time.
In 2026, what GPU or LLM tech has actually achieved both? Seriously, name one.
Or is your argument just “I can’t run it at home, therefore the model is bad”?
And apparently Z.AI somehow owes you a model that’s insanely capable, tiny enough to fit on your consumer GPU, and open-sourced for free?
That’s not a technical expectation. That’s just entitlement.
always somebody complaining...
So now a model doesn’t deserve to be called “Flash” unless it can run on consumer hardware? Lmao.
The funniest part is that the entire meltdown from people like you comes from your little fantasy not becoming reality: you want ultra-low VRAM usage AND Opus 4.8-tier performance at the same time.
In 2026, what GPU or LLM tech has actually achieved both? Seriously, name one.
Or is your argument just “I can’t run it at home, therefore the model is bad”?
And apparently Z.AI somehow owes you a model that’s insanely capable, tiny enough to fit on your consumer GPU, and open-sourced for free?
That’s not a technical expectation. That’s just entitlement.
Everyone is saying “z.ai, thank you!” But just a part of the community and fans were expecting a smaller model and its derivative distillation. Like it was with 4.7 flash, but in the end they got a monster that can barely run on an ultra‑top‑tier user config. Essentially, people were expecting an analogue of Qwen 3.8 27B 35B‑A3B and were deeply disappointed. You also need to hear these people... No one is saying that the model is bad or that z.ai did the wrong thing by releasing it... but perhaps it’s possible to create a glm 5.3 mini 20-36b, since flash models now look like giants compared to a year ago. Still, user capacities are not growing as fast as server racks under Vera Rubin.
The funniest part is that the entire meltdown from people like you comes from your little fantasy not becoming reality: you want ultra-low VRAM usage AND Opus 4.8-tier performance at the same time.
No, nobody said that. We want SOMETHING modern that can run on consumer hardware, like GLM-4.7-Flash or Qwen-3.5 35B-a3b or Gemma. At this moment only Zai can make a good model of that size to rival Qwen.
So now a model doesn’t deserve to be called “Flash” unless it can run on consumer hardware? Lmao.
The GLM naming scheme is simple:
- GLM x.x for new SOTA models.
- GLM x.x Air for models around 100b
- GLM x.x Flash for models that runs on computer hardware (GLM 4.7 Flash for example is 30b-a3b MoE)
- GLM x.x Turbo is their "Faster than SOTA, but slower and bigger than Air".
GLM 5.3 Flash is not a Flash, but Turbo.
Our frustration is that probably this means that we won't get a small model from them again, because their models series for consumer hardware is WAY out of range of any consumer hardware.
The funniest part is that the entire meltdown from people like you comes from your little fantasy not becoming reality: you want ultra-low VRAM usage AND Opus 4.8-tier performance at the same time.
Nobody said that. Maybe your LLM has allucinated when you gave the prompt "Answer them with an argument that is impossible for them to win. Make it perfect, with no mistakes."
We just want a comparable model to qwen 3.6 35b-a3b, because they are alone in that range from months now and only Z.ai can make a great model to compete. More competition is always better, but Qwen stands alone at the top of the local hardware model tier.
Or is your argument just “I can’t run it at home, therefore the model is bad”?
Your model hallucinated again. We never said it was bad.
And apparently Z.AI somehow owes you a model that’s insanely capable, tiny enough to fit on your consumer GPU, and open-sourced for free?
They don't owe us anything, but labeling their model as one thing and not delivering it is just... messed up. They could have named it GLM 5.3 Turbo and that would be okay, but naming it the same as their consumer hardware models (their lowest-parameter models) is the same as saying that this is their new lowest-parameter model from now on, nothing below these parameters. Or nothing much below them (like a 30B MoE).
That’s not a technical expectation. That’s just entitlement.
Yeah, we already know you're using LLM to write your answers.