Elon Musk's AI assistant Grok boasted that the billionaire had the "potential to drink piss better than any human in history," among other absurd claims.
I once tried a community version from huggingface (distilled), which worked quite well even on modest hardware. But that was a while ago. Unfortunately, I haven’t had much time to look into this stuff lately, but I wanted to check that again at some point.
naw, I mean more that the kind of people who uncritically would take everything a chatbot says a face value are probably better off being in chatGPTs little curated garden anyway. Cause people like that are going to immediately get grifted into whatever comes along first no matter what, and a lot of those are a lot more dangerous to the rest of us that a bot that won’t talk great replacement with you.
Ahh, thank you—I had misunderstood that, since Deepseek is (more or less) an open-source LLM from China that can also be used and fine-tuned on your own device using your own hardware.
Do you have a cluster with 10 A100 lying around? Because that’s what it gets to run deepseek.
It is open source, but it is far from accessible to run on your own hardware.
I have a Ryzen 7800 gaming destkop, RTX 3090, and 128GB DDR5. Nothing that unreasonable. And I can run the full GLM 4.6 with quite acceptable token divergence compared to the unquantized model, see: https://huggingface.co/Downtown-Case/GLM-4.6-128GB-RAM-IK-GGUF
If I had a EPYC/Threadripper homelab, I could run Deepseek the same way.
Yes, that’s true. It is resource-intensive, but unlike other capable LLMs, it is somewhat possible—not for most private individuals due to the requirements, but for companies with the necessary budget.
They’re overestimating the costs. 4x H100 and 512GB DDR4 will run the full DeepSeek-R1 model, that’s about $100k of GPU and $7k of RAM. It’s not something you’re going to have in your homelab (for a few years at least) but it’s well within the budget of a hobbyist group or moderately sized local business.
Since it’s an open weights model, people have created quantized versions of the model. The resulting models can have much less parameters and that makes their RAM requirements a lot lower.
You can run quantized versions of DeepSeek-R1 locally. I’m running deepseek-r1-0528-qwen3-8b on a machine with an NVIDIA 3080 12GB and 64GB RAM. Unless you pay for an AI service and are using their flagship models, it’s pretty indistinguishable from the full model.
If you’re coding or doing other tasks that push AI it’ll stumble more often, but for a ‘ChatGPT’ style interaction you couldn’t tell the difference between it and ChatGPT.
It’s why I trust my random unauditable chinese matrix soup over my random unauditable american matrix soup frankly
Trusting any of that shit is the problem.
You mean Deepseek on a local device?
Most aren’t really running Deepseek locally. What ollama advertises (and basically lies about) is the now-obselete Qwen 2.5 distillations.
…I mean, some are, but it’s exclusively lunatics with EPYC homelab servers, heh. And they are not using ollama.
Thx for clarifying.
I once tried a community version from huggingface (distilled), which worked quite well even on modest hardware. But that was a while ago. Unfortunately, I haven’t had much time to look into this stuff lately, but I wanted to check that again at some point.
naw, I mean more that the kind of people who uncritically would take everything a chatbot says a face value are probably better off being in chatGPTs little curated garden anyway. Cause people like that are going to immediately get grifted into whatever comes along first no matter what, and a lot of those are a lot more dangerous to the rest of us that a bot that won’t talk great replacement with you.
Ahh, thank you—I had misunderstood that, since Deepseek is (more or less) an open-source LLM from China that can also be used and fine-tuned on your own device using your own hardware.
Do you have a cluster with 10 A100 lying around? Because that’s what it gets to run deepseek. It is open source, but it is far from accessible to run on your own hardware.
That’s not strictly true.
I have a Ryzen 7800 gaming destkop, RTX 3090, and 128GB DDR5. Nothing that unreasonable. And I can run the full GLM 4.6 with quite acceptable token divergence compared to the unquantized model, see: https://huggingface.co/Downtown-Case/GLM-4.6-128GB-RAM-IK-GGUF
If I had a EPYC/Threadripper homelab, I could run Deepseek the same way.
Yes, that’s true. It is resource-intensive, but unlike other capable LLMs, it is somewhat possible—not for most private individuals due to the requirements, but for companies with the necessary budget.
They’re overestimating the costs. 4x H100 and 512GB DDR4 will run the full DeepSeek-R1 model, that’s about $100k of GPU and $7k of RAM. It’s not something you’re going to have in your homelab (for a few years at least) but it’s well within the budget of a hobbyist group or moderately sized local business.
Since it’s an open weights model, people have created quantized versions of the model. The resulting models can have much less parameters and that makes their RAM requirements a lot lower.
You can run quantized versions of DeepSeek-R1 locally. I’m running deepseek-r1-0528-qwen3-8b on a machine with an NVIDIA 3080 12GB and 64GB RAM. Unless you pay for an AI service and are using their flagship models, it’s pretty indistinguishable from the full model.
If you’re coding or doing other tasks that push AI it’ll stumble more often, but for a ‘ChatGPT’ style interaction you couldn’t tell the difference between it and ChatGPT.
You should be running hybrid inference of GLM Air with a setup like that. Qwen 8B is kinda obsolete.
I dunno what kind of speeds you absolutely need, but I bet you could get at least 12 tokens/s.