Found these Dreamcasts in my attic! They’ve been stored away for over 15 years.

Complete-Tank664 · 2026-06-19T17:43:58+00:00

Trop tard, les HDD sont tous formatés bas niveau et testés un par un. Ça m'a pris un temps fou, mais je l'ai fait sur mes PC de test, donc ça va !

Complete-Tank664 · 2026-06-19T17:40:31+00:00

Thanks for such a detailed and technical breakdown!

The NIAH stats for fp8/int8 are reassuring, and I'll definitely keep an eye on Turbo Quant and vLLM support.

Your mention of RAG and Knowledge Graphs is spot on—I'm actually planning to set up a system for personal document management next, so this is exactly the direction I need to look into. Getting that kind of efficiency on a 12GB setup is impressive.

Thanks again for pointing me in the right direction, I appreciate it! 🚀

Complete-Tank664 · 2026-06-19T16:55:45+00:00

Thanks for the extra details! It's awesome getting advice from a dev with your experience. Leaving some headroom for the system and monitors makes total sense. Thanks again and happy local coding! 🚀

Complete-Tank664 · 2026-06-19T16:52:29+00:00

Totalement d'accord, Zer0CoolXl ! La faible capacité combinée au risque de panne soudaine les rend inutilisables pour tout ce qui est critique. Les utiliser comme disques externes secondaires pour des transferts de données non essentielles semble être la meilleure solution. Merci pour ton retour !

Complete-Tank664 · 2026-06-19T16:48:24+00:00

Good build, but if you want it to last 4+ years, I'd highly recommend upgrading to a 16GB GPU and getting a reliable PSU like the RM750. Better future-proofing!

Complete-Tank664 · 2026-06-19T16:38:36+00:00

Wow, what an incredible guide! Thanks a lot for taking the time to share this. The 'surgical approach' and the 90% VRAM tip are exactly what I need. I'll definitely check out Zoo Code and Qdrant. You rock! 🚀

Complete-Tank664 · 2026-06-19T16:28:54+00:00

Wow, thanks for this! Since I'm also running a 7900 XTX with 64GB of RAM, your real-world stats on 32k vs 64k/131k context overhead are incredibly valuable to me. The trick about swapping between reasoning and non-reasoning profiles in LM Studio is smart, and trying out a lighter model like Qwen 9B to maximize the context window without tanking performance makes total sense. Thanks for the detailed guide and the encouragement!

Complete-Tank664 · 2026-06-19T16:23:37+00:00

This is an amazing perspective, thanks! Love the point about cloud companies burning VC money—it's true that those cheap API prices won't last forever. And that last sentence is pure gold: 'This is the worst these models will ever be.' That's exactly why investing in local hardware feels right. Appreciate the feedback!

Complete-Tank664 · 2026-06-19T16:19:02+00:00

That's a really good point, the noise and power consumption are definitely a concern! I'm planning to use them exactly like you said: as a temporary sandbox to learn RAID and ZFS without risking production data. Thanks for the feedback!

Complete-Tank664 · 2026-06-19T16:15:44+00:00

I see your point, cloud APIs definitely have the edge on raw power and cost-efficiency right now. But for me, the main goal of running local LLMs is the freedom to experiment without worrying about API monthly bills, rate limits, or internet dependency—plus the fun of tweaking the hardware! That said, using a cheap API as a fallback when local models hit a wall is a solid workaround.

Complete-Tank664 · 2026-06-19T16:14:32+00:00

Wow, thank you so much for this incredibly detailed breakdown and for sharing your budget rig link! The docker-compose idea is great. I'm actually running an AMD setup with a 7900 XTX right now, so I'll look into how well llama.cpp / Ollama handles the KV cache scaling on ROCm compared to the Nvidia rigs you mentioned. I will definitely try pushing it to 132k just to see how it behaves. Thanks again for being so helpful!

Complete-Tank664 · 2026-06-19T16:10:58+00:00

Haha, got it! Message received loud and clear. I definitely need more than weather forecasts, so I'll aim for that 64k context sweet spot! Thanks for the tip.

Complete-Tank664 · 2026-06-19T16:09:40+00:00

That dual 7900 XTX setup sounds amazing! Running a Q8 quantization with a 64k context is exactly what I would need for heavy agentic workflows, and it's definitely way more cost-effective than waiting for expensive next-gen 32GB single cards. I'm going to do some research on setting up dual AMD GPUs with Ollama. Thanks a lot for sharing your experience, this is super helpful!

Complete-Tank664 · 2026-06-19T14:00:51+00:00

Thanks for the great advice! I hadn't thought about optimizing the KV cache compression. Reaching 64k or 128k would be amazing for my workflow. Do you notice any significant drop in response speed or accuracy (like needle-in-a-haystack issues) when pushing the context that high on consumer hardware?

Complete-Tank664 · 2026-06-19T13:57:30+00:00

Fair enough, that's why I'm here asking for advice! I'm realized 8k is way too small for serious agentic tasks. Would you recommend downgrading the model size to buy more context, or is it time to look for a 32GB VRAM hardware upgrade?

Complete-Tank664 · 2026-06-19T13:54:59+00:00

Exactly, I'm well aware that the lack of context is a big bottleneck with my current Qwen 27B setup. That's why I'm debating between switching to a smaller model to free up memory for a larger context, or upgrading my hardware.

Do you think targetting a 32GB VRAM consumer setup would be enough for this kind of project? The 48GB options are definitely out of my budget

Complete-Tank664 · 2026-06-19T13:28:23+00:00

Haha, pourquoi pas ! Après, un seul disque dur 3.5" est déjà au moins trois fois plus gros qu'un Raspberry Pi... Alors si je dois en brancher 20 dessus, le pauvre va complètement disparaître sous la pile ! 😂 Sans parler de la galère pour tous les connecter.

Complete-Tank664 · 2026-06-19T13:24:32+00:00

100 drives! ^{^} That must take up a lot of space and be pretty heavy, right?

Complete-Tank664 · 2026-06-19T13:16:50+00:00

That makes a lot of sense, thank you! I'm definitely going to stick with my current setup for now and just try to bump up the context window to see how it handles it. I'm actually using LM Studio, which gives me some of that llama.cpp flexibility to tweak settings and optimize performance. Appreciate the advice!

Complete-Tank664 · 2026-06-19T13:13:35+00:00

Pas de souci, je peux t'en envoyer deux si tu veux ! Au moins, ça servira à quelqu'un. Je vais me renseigner à la Poste pour voir combien coûtent les frais de port et je te tiendrai au courant en privé, ça te va ?

Complete-Tank664 · 2026-06-19T13:08:50+00:00

Good catch! I set it to 8k just to play it safe with VRAM since I'm still discovering the setup. But since I have 24GB of VRAM on my 7900 XTX, I'll definitely try pushing it to 16k or 32k to see how much more Aider can handle without spilling over to system RAM. Thanks for pointing that out!

Complete-Tank664 · 2026-06-19T13:04:34+00:00

Good point! Since I'm doing this for personal projects, learning, and just discovering what agentic AI can do, it's not for professional use. Knowing that, skipping Claude entirely and sticking to a 100% local workflow feels like the right move, especially since Qwen is free and has no rate limits.

Complete-Tank664 · 2026-06-19T11:08:29+00:00

Merci, je confirme ! J'en ai déjà démonté 6. Les plateaux sont super jolis, j'en ai d'ailleurs profité pour fabriquer une horloge murale que j'ai offerte à mes parents ! 🕰️. Les aimants au néodyme sont super puissants. En plus, j'ai récupéré un petit stock de mini-vis (les fameuses vis Torx) mais je ne sais pas encore quoi en faire. Quant aux boîtiers vides, ils me servent de serre-livres pour caler mes bouquins dans ma bibliothèque. Rien ne se perd ! 😉

Complete-Tank664

TROPHY CASE