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I think in one video it looked like 16 cards. I think he did multiple bifurcations of the pcie lanes. I think he is / was using it for protein folding as well.

My MacBook Air with 24GB of unified RAM is enough to run something simple and useful.

I use an 6700 XTX and it's working perfectly fine, depending on the model. Gemma4 takes a long time to generate answers, but the Qwen-Series is quick and starts generating answers in ~10 seconds.

16GB is plenty for even older model setups. Now they've got a few models designed so you load just parts of the model onto the GPU (Mixture of Experts) and use the CPU for less referenced sections, so you get both reasonable speed and a much more complex model.

For chat usage (which is strictly a more efficient way to generate code on the LLM's part, although you have to keep carefully guided and compartmentalized otherwise it typically requires a lot more testing and sometimes back-and-forth iteration on your part) 12GB is plenty to run many decent LLMs, you'll typically want to use a Q4 quantization to make models with larger parameter fit into smaller memory, sometimes an IQ2 or IQ3 if you really want a particular model.

For agentic usage (where the LLM is trained and optimized to use a harness like this to start requesting tool calls and getting their results and using the results of the tool calls to inform what it's trying to do) it's quite a bit more challenging to do on consumer hardware at a tolerable speed. The tools often generate large amounts of output which then take a long time to process, and the models and harnesses are both typically quite a bit stupider about using your limited resources efficiently. If you're using to commercial "frontier" agentic models like Claude Code you're going to have a bad time.

That said, it is absolutely possible to do agentic AI on consumer hardware (just the GPU you have, not 6 of them), as long as you're reasonably patient, using a harness properly tuned for efficiency. Out-of-the-box, many if not most are designed for remote API usage, even the "open source, local" ones realistically rely on free tier APIs and are inherently wasteful in terms of them not really caring how many tokens you burn in these remote datacenters and they're expecting to just be able to iterate over and over again until they get it right. You don't have that luxury when you're getting slow tokens.

Is PewDiePie's any better or more efficient? I don't know, I haven't tried it yet. I prefer more minimal harnesses personally, OpenCode is about the most usable I've found personally, although I'm starting to experiment with Pi-mono (called Pi, but that's unsearchable) which seems very promising, and I know quite a few people who have had good successful agent usage with Hermes Agent.

I'm not going to pretend it's going to be easy or that you'll necessarily have very good results. I am pretty lukewarm on AI as a whole, but I am personally deeply invested in making sure I have fully local access to it in as much capacity as is currently technologically possible, as a personal digital sovereignty issue.

As for hardware, I have a 12GB card myself and you don't really need to fit everything into VRAM these days. I have an AMD X3D CPU which allows me to offload some of the model to system RAM with pretty decent performance, maybe it's prohibitive on different architectures or configurations I don't know but it's worth a try. glm-4.7-flash:Q4_K_M from ollama is the model I've had the most consistent success with and with ollama running it with the context window set to 50,000 (context should also be set to be quantized to Q4_K_M), I end up with almost half of it offloaded to system RAM and it still runs quite fast thanks to the flash attention feature. I've worked with gemma4 quite a lot too and it's definitely really fast but it's also a bit unstable/weird at times, at least the heretic version hf.co/Stabhappy/gemma-4-26B-A4B-it-heretic-GGUF:Q4_K_M I'm running is. Still, if you really do need to fit everything into a smaller set of RAM you might try the gemma4 E4B models which clock in around 9GB when quantized. Qwen3.6 is I guess supposed to be really good too and should fit nicely on your 12GB card, but I haven't had much opportunity to play with it yet. Qwen3 and 3.5 felt rather disappointing to me for agentic use but YMMV.

You're not completely going to outsource all software and all code you write to AI using a local model, the way companies are doing with those commercial models. But I consider that an advantage, not a flaw. I find it's much more useful to have it help, suggest and advise, not to completely replace everything I'm doing. Yes, sometimes it's slow and sometimes it's wrong, but so are other people when I ask them sometimes. I'm prepared for it, and you should be too. Don't get complacent.

I can tell you from personal experience, 8GB is not enough for a snappy experience. Maybe if you had it setup to churn through data overnight. My RTX 3060 Ti was not happy.

Depends on what you want it to do and how well it should do it. Zero is potentially enough. A second hand card from half a decade ago can also do quite a lot.

I have a rx5600xt (6gb), 32gb ram, ryzen 3600. System hasn't been updated since i built it during covid. QwenV3-vl35B is the heftiest thing I can run, it gets around 2 tokens/sec, in LM studio. It's easier than most people seem to think.

My buddy has an older 16GB card and I installed LM studio for fun. Its not quite as fast as some of the web-based ones, but perfectly usable.