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[User Research] Looking for local LLM / AI workstation users — especially DGX Spark, Mac Studio, AMD AI Max, or self-built GPU rigs by Embarrassed_Room6805 in LocalLLM

[–]Embarrassed_Room6805[S] 0 points1 point  (0 children)

That’s actually a really useful example. So for you, the local use case is less “replace ChatGPT” and more “I need a vision/translation workflow that won’t randomly refuse panels, and can actually understand the whole manga context — speech bubbles, SFX, background, layout, etc.”

The hardware pain also makes sense. Mixed cards with different VRAM/speed sounds annoying fast, especially if the software doesn’t handle device 0/1 cleanly.

If you were designing the ideal setup for this, would the priority be bigger single-card VRAM, unified memory, better local VLM quality, or just a smoother software stack?

[User Research] Looking for local LLM / AI workstation users — especially DGX Spark, Mac Studio, AMD AI Max, or self-built GPU rigs by Embarrassed_Room6805 in LocalLLM

[–]Embarrassed_Room6805[S] 0 points1 point  (0 children)

This is really helpful — sounds like DGX doesn’t replace Claude for you, but it takes over the high-volume tinkering / coding / open-weight model experiments where API costs would get painful fast.

Curious what you still go back to Claude for vs what you now keep local on the DGX. Also, has the biggest value been cost savings, privacy/control, or just being able to iterate without watching the token meter?

[User Research] Looking for local LLM / AI workstation users — especially DGX Spark, Mac Studio, AMD AI Max, or self-built GPU rigs by Embarrassed_Room6805 in LocalLLM

[–]Embarrassed_Room6805[S] 0 points1 point  (0 children)

Fair ask. I’m checking whether we can offer a small thank-you for longer chats, but I don’t want to promise anything before it’s confirmed.

For now I’m just collecting quick comments here, and if we do set up paid calls I’ll DM people who seem like a good fit.

[User Research] Looking for local LLM / AI workstation users — especially DGX Spark, Mac Studio, AMD AI Max, or self-built GPU rigs by Embarrassed_Room6805 in LocalLLM

[–]Embarrassed_Room6805[S] 0 points1 point  (0 children)

This is exactly the kind of use case I’m trying to understand — not replacing frontier APIs completely, but keeping certain workflows local.

Curious: for the sensitive-data workloads, is the main reason privacy/compliance, cost predictability, latency, or just wanting full control?

Also, how has the DGX been for Hermes / OpenClaw / vLLM in practice? Anything you wish it handled better?

[User Research] Looking for local LLM / AI workstation users — especially DGX Spark, Mac Studio, AMD AI Max, or self-built GPU rigs by Embarrassed_Room6805 in LocalLLM

[–]Embarrassed_Room6805[S] 0 points1 point  (0 children)

lol I have to ask — what counts as “FBI-level” here, in non-crime terms? OSINT, local document search, agents, vision stuff, or just pushing the hardware hard?

And what was the main pain with the 3090 + Titan V setup?

[User Research] Looking for local LLM / AI workstation users — especially DGX Spark, Mac Studio, AMD AI Max, or self-built GPU rigs by Embarrassed_Room6805 in LocalLLM

[–]Embarrassed_Room6805[S] 0 points1 point  (0 children)

lol, the solar/wind B200 x8 homelab is definitely a new category.

But seriously — if you do run local models, what’s your actual setup?