# Deploy LLM on Space with efficient GPU

**URL:** <https://discuss.huggingface.co/t/deploy-llm-on-space-with-efficient-gpu/168639>\
**Category:** Spaces\
**Created:** [September 24, 2025, 2:49pm UTC](https://discuss.huggingface.co/t/deploy-llm-on-space-with-efficient-gpu/168639 "2025-09-24T14:49:25Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![cdupland](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/cdupland/32/37492_2.png) [@cdupland](https://discuss.huggingface.co/u/cdupland)\
**Post date:** [September 24, 2025, 2:49pm UTC](https://discuss.huggingface.co/t/deploy-llm-on-space-with-efficient-gpu/168639/1 "2025-09-24T14:49:25Z")

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Hi guys,  
I mainly use HuggingFace to buid AI Agent with Streamlit and LangChain and deploy them on Space.  
Today, I would like to use Space to deploy an open model (Mixtral-8x7B-Instruct-v0.1) for my client.  
The goal is to make it accessible with an API (FastAPI) like other providers (OpenAI, Mistral, …)  
No problem for that, but I would like to add some harware with GPU by upgrading Space.  
How can I choose the right setting to don’t have an expensive invoice ?  
Is that better to use an allocated hardware (more expensive, but more secure, no latency) or by using ZeroGPU subscription (latency) ?

Thanks

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<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [September 24, 2025, 10:46pm UTC](https://discuss.huggingface.co/t/deploy-llm-on-space-with-efficient-gpu/168639/2 "2025-09-24T22:46:03Z")

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There’s a daily usage limit, but Zero GPU is overwhelmingly cost-effective and offers a flat rate instead of pay-as-you-go. The specs are basically **nVidia H200, and you can use over 70GB of VRAM… Spaces also has good CPU and RAM**.

However, [the implementation is quite quirky](https://huggingface.co/docs/hub/spaces-zerogpu), so I recommend Zero GPU [only if you look at someone else’s code and think you can manage it](https://huggingface.co/spaces?sort=trending&hardware=zerogpu).

For other regular PAYG GPUs, you could save money by quantizing your model and using a cheaper GPU with smaller VRAM…  
There’s probably no usage cap, so if you don’t sleep or pause frequently, it will cost you…