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Show HN: Can I run this LLM? (locally)

can-i-run-this-llm-blue.vercel.app

21–30 of 55 posts

Re: Show HN: Can I run this LLM? (locally)

#21
post #15

- When you press the refresh button, it loads data from huggingface.co/api, doing the same request seemingly 122 times within one second or so - When I select "no dedicated GPU" because mine isn't listed, it'll just answer the same "you need more (V)RAM" for everything I click. It might as well color those models red in the list already, or at minimum show the result without having to click "Check" after selecting ev…

thanks for the feedback, will check and update if there are any bugs causing multiple calls

Re: Show HN: Can I run this LLM? (locally)

#23
post #21
post #15

- When you press the refresh button, it loads data from huggingface.co/api, doing the same request seemingly 122 times within one second or so - When I select "no dedicated GPU" because mine isn't listed, it'll just answer the same "you need more (V)RAM" for everything I click. It might as well color those models red in the list already, or at minimum show the result without having to click "Check" after selecting ev…

thanks for the feedback, will check and update if there are any bugs causing multiple calls

In case it's relevant, I'm using Firefox

Re: Show HN: Can I run this LLM? (locally)

#26

And herein lies the problem with vibe coding - accuracy is wanting. I can absolutely run models that this site says cannot be run. Shared RAM is a thing - even with limited VRAM, shared RAM can compensate to run larger models. (Slowly, admittedly, but they work.)

New word for me: vibe coding

> coined the term in February 2025

> Vibe coding is a new coding style [...] A programmer can describe a program in words and get an AI tool to generate working code, without requiring an understanding of the code. [...] [The programmer] surrenders to the "vibes" of the AI [without reading the resulting code.] When errors arise, he simply copies them into the system without further explanation.

https://en.wikipedia.org/wiki/Vibe_coding

Re: Show HN: Can I run this LLM? (locally)

#28
> Can I Run DeepSeek R1

> Yes, you can run this model! Your system has sufficient resources (16GB RAM, 12GB VRAM) to run the smaller distilled version (likely 7B parameters or less) of this model.

Last I checked DeepSeek R1 was a 671B model, not a 7B model. Was this site made with AI?

Re: Show HN: Can I run this LLM? (locally)

#29
post #20
post #9

You forgot to define what constitutes “running”. And people have different expectations.

agree, the model assumes a multitasking setup where you need some leftover ram for other tasks. You can squeeze in much larger models when running dedicated

It would be a lot nicer if it would not just give a binary flag "can/can't run" but what to expect.

Ideal scenario (YMMV): add more hardware parameters (like chipset, CPU, actual RAM type/timings - with presets for most common setups) and extra model settings (quantization and context size come to mind) then answer like this: "you have sufficient RAM to load the model, and you should expect performance around 10 tok/sec with 3s to the first token". Or maybe rather list all models you know about and provide performance for each. Inverse search ("what rig do I need to run this model with at least this performance") would be also very cool. May be nice have an ability to parse input of common system information tools (like Windows wmic/Get-ComputerInfo, macOS system_profiler or GNU/Linux dmidecode - not sure if all info is there, but just as an rough idea: give some commands to run, parse their output in search of specs)

Of course, this would be very non-trivial to implement and you'll probably have to dig a lot for anecdotal data on how various hardware performs (hmm... maybe a good task for agentic LLM?) but that would actually make this a serious tool that people can use and link to, rather than a toy.

Re: Show HN: Can I run this LLM? (locally)

#30

> Can I Run DeepSeek R1 > Yes, you can run this model! Your system has sufficient resources (16GB RAM, 12GB VRAM) to run the smaller distilled version (likely 7B parameters or less) of this model. Last I checked DeepSeek R1 was a 671B model, not a 7B model. Was this site made with AI?

> > smaller distilled version

Not technically the full R1 model, it’s talking about the distillations where Deepseek trained Qwen and Llama models based on R1 output

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