AI Workstations

An AI workstation mini PC is a small-footprint machine built around one number that matters more than any other: how much fast memory it can hold. Part of our broader Mini PCs lineup, this range is aimed specifically at developers, researchers, and small businesses who want to run large language models on their own hardware instead of sending every prompt to a cloud API.

Get the memory capacity and bandwidth right and everything else, from software to cooling, tends to fall into place; get it wrong and you spend your evenings fighting out-of-memory errors instead of running models.

The three numbers that actually decide what you can run

Strip away the marketing and local AI performance comes down to three things. Memory capacity decides what fits, a model has to load fully into memory before it can run at all, so a 7B parameter model needs roughly 16GB, a 14B model wants 32GB as a practical floor, and 32B-plus models need either a large unified-memory box or a discrete GPU with genuine VRAM.

Memory bandwidth decides how fast the machine generates each token once the model is loaded, which is why unified-memory architectures that avoid a CPU-to-GPU transfer bottleneck often outperform boxes with a faster CPU but slower memory. Storage speed matters too, since model files run from tens to well over a hundred gigabytes each, and a fast NVMe drive keeps load times and disk swapping from becoming the bottleneck instead.

Matching the box to the model size

If you mostly want a capable local assistant for coding help, drafting, or offline notes, a machine with 32GB of RAM comfortably runs 7B to 8B parameter models quantized, which is a realistic and genuinely useful starting point for most individual buyers.

Stepping up to 14B models is where things get serious, and 32GB should be treated as a floor rather than a target, with 64GB giving real headroom for longer context windows and multiple models loaded at once. Anything at 32B parameters and above is advanced territory best suited to machines with 96GB or more of unified memory, or a dedicated GPU-class card, and is worth testing against your specific workload before you commit to a purchase.

Software that actually runs on these boxes

Most AI workstation mini PCs in this range work out of the box with Ollama, LM Studio, or llama.cpp, all of which handle model downloading, quantization, and inference through a straightforward interface rather than a command-line-only workflow.

Open WebUI and AnythingLLM sit on top of these runtimes for teams that want a browser-based chat interface or document search across a small office, which is a common setup for businesses running a private, offline assistant rather than a public cloud tool.

What to check before buying

RAM ceiling and RAM type matter more here than clock speed, so confirm both the maximum supported capacity and whether it is user-upgradeable, since some compact chassis solder memory to the board and lock in whatever configuration you buy.

Cooling and sustained power draw are worth a second look too: a chip that throttles under a long inference session will run noticeably slower than its spec sheet suggests, so a chassis with genuine airflow headroom, not just a large heatsink, tends to perform better in practice. If your workload will eventually need real GPU acceleration rather than CPU or integrated-graphics inference, check whether the model you are considering supports an external GPU enclosure or ships with dedicated graphics silicon built in.

Power draw and running costs

Unlike a gaming tower with a discrete GPU pulling several hundred watts under load, most AI workstation mini PCs in this range sit comfortably under 100W even during sustained inference, which matters if the machine is going to sit powered on for hours at a time serving a team's queries. That lower running cost is one of the strongest arguments for local inference in the first place: once the hardware is paid for, a query costs whatever the electricity meter says, not a per-token cloud bill. Check the manufacturer's rated TDP against your case airflow before committing to a fanless or ultra-compact chassis, since sustained AI workloads run hotter for longer than a typical office task ever will.

Why buy your AI workstation from PonDesk

We list the real, verified RAM ceiling and memory type for every machine in this range rather than a headline spec that turns out to be a maximum you cannot actually order, which matters enormously for a purchase where memory capacity is the whole decision. Our team can talk through which model size you are targeting before you buy, so you are not left discovering the RAM limit only after the box arrives. We source directly from manufacturers and ship to the UK, US and further afield, with support on hand if you need help getting Ollama or LM Studio running once the machine lands.

How much RAM do I need to run a 7B parameter model locally?

A quantized 7B parameter model runs comfortably with 16GB of system RAM, though 32GB gives more headroom for longer conversations and running other applications alongside it. This is the realistic entry point for most local AI buyers rather than a stripped-down minimum.

Can a mini PC really compete with a dedicated GPU for running LLMs?

For models that fit within its unified memory, a well-specified AI workstation mini PC can match or beat an older discrete GPU on inference speed, since it avoids the memory-transfer bottleneck that slows GPU setups down when a model is too large for VRAM alone. Where a discrete GPU still wins outright is raw throughput on smaller models that comfortably fit in its VRAM.

Do I need a graphics card at all for local AI inference?

No, many AI workstation mini PCs rely entirely on unified memory and an integrated or built-in AI-focused chip rather than a separate graphics card, and this is often the more memory-efficient route for models in the 7B to 70B range. A discrete GPU still helps for image generation workloads or when you want the fastest possible tokens-per-second on models that fit its VRAM.

If your priority is virtual machines rather than local models, our Hypervisor mini Server/Workstations range is built for that job instead, and both ranges sit under our full Mini PCs lineup. Get in touch for a quote or spec advice before you buy.