Show off your current setup, whatever you're actually running AI or quantum SDK workloads on

[Marcus82]

Not asking for a dream rig, asking what's genuinely sitting on your desk or in your cloud console right now doing the actual work. Local GPU, rented cloud instance, a quantum simulator running on a laptop that's definitely not built for it, all fair game

[DigitalNomad62]

Running everything through a cloud instance now, gave up on local hardware entirely once rental pricing actually made sense for my usage pattern

[NeonPhantom32]

Modest local GPU for smaller experiments, anything serious gets shipped off to cloud compute, feels like the sensible hybrid setup

[TheGame_Fan]

Genuinely running a quantum simulator on a laptop that groans audibly past a certain qubit count, works fine for learning purposes though

[CrimsonNova28]

Home lab setup that's probably overkill for what I actually need, but the tinkering itself is honestly half the appeal for me

[Sharon79]

Everything's just API calls now, haven't touched local hardware in over a year, the abstraction layer has gotten good enough that it barely matters anymore

[Elliot_67]

A modest local setup does most of the daily work here, paired with a mid-range GPU and 32GB RAM.

Smaller models and experiments run smoothly, while anything heavier gets pushed to the cloud.

That hybrid approach keeps things flexible without overspending

[ForumGremlin]

Most of the heavy lifting happens in the cloud rather than locally.

The desk machine is basically a capable front-end, while rented GPUs handle the real workload.

Less hands-on, but scaling is effortless when needed.

[Ben55]

Tinkering ended up being the main motivation behind building a home lab.

Multiple GPUs, plenty of storage, arguably overkill for actual usage.

Still worth it just for the process itself

[Lucy_35]

A decent laptop carries more of the load than expected.

With the right optimizations, it handles development and smaller tasks reliably.

Portability ends up being a bigger advantage than raw power.

[HeartbreakKid_Fan]

Splitting work between local and cloud systems tends to work best.

Quick tests run locally, while longer training jobs move off-machine.

That balance keeps things efficient

[DeadChat58]

An old gaming PC got a second life as an AI workstation.

With a few upgrades and tweaks, it still performs surprisingly well.

Nice way to stretch existing hardware.

[Aidan75]

For quantum SDK work, the local machine is mostly just a staging area.

Simulations run locally, but real hardware access is entirely remote.

That setup feels pretty standard now.

[Karen_37]

Running everything on a single GPU setup means pushing limits constantly.

Memory becomes the main constraint, forcing more careful planning.

Not ideal, but it teaches efficiency.

[Amber Tiger]

A small shared cluster handles team workloads.

Nothing massive, but enough to distribute jobs when needed.

Coordination ends up being the harder part than the hardware.

[Python35]

Simulation-heavy workflows stay local for convenience.

When things scale up, cloud resources take over.

Keeps hardware demands manageable overall.

[CMPunk_Mike]

The build evolved gradually rather than all at once.

Adding components over time made it easier to adapt.

The setup reflects actual needs instead of guesses.

[BillyBob]

A separate GPU server sits away from the main workspace.

Keeping heat and noise out of the room improves daily comfort.

Practical benefit that's easy to overlook

[GoalMachine]

Smaller edge devices are part of the setup for testing.

Running models under constraints gives a different perspective.

Not everything needs a powerful rig.

[Marnie80]

Development happens on a MacBook, while execution moves to the cloud.

Keeping those roles separate simplifies the workflow.

Less clutter, fewer complications.

[ActivationRune]

In the end, matching hardware to workflow matters more than chasing specs.

A balanced system that fits how you work tends to outperform raw power alone

[FirstBen12]

Stability and cooling became the focus for a custom build.

Long-running tasks demand consistency more than peak performance.

Thermals quietly become a priority over time.

[SingularityNode Hare]

A budget-conscious build still gets meaningful work done.

Used parts and gradual upgrades keep costs down.

Performance is good enough with the right expectations.