Best hardware for running local LLMs in 2026: GPU box vs 128GB unified-memory mini PC

The most important local-AI upgrade is enough memory to keep your chosen model on the GPU. Everything after that is about how quickly it can read those weights, process your prompt and get the answer onto the screen. A bigger model that spends its day spilling into slow memory is a surprisingly effective way to miss your old laptop.

Choose an ASUS Dual RTX 5060 Ti 16GB for a desktop running 7–14B models at Q4. Choose the GMKtec EVO-X2 with 128GB RAM and a 2TB SSD when you want room for 32B and 70B models in one compact machine. A 24GB NVIDIA card is the useful middle step for 32B Q4; our GPU buying guide covers those cards.

Maker specs, our marked bench results and explicitly labelled calculations supply the numbers below.

Already own a mini PC or laptop you like? Our OCuLink vs Thunderbolt/USB4 GPU dock guide compares three docks, separate versus included power supplies, host fit and how to confirm the model is GPU-resident.

For a slim workstation, our RTX 2000 Ada vs RTX A2000 guide compares low-profile 16GB and 12GB cards, with a two-slot fit checklist and a worked bandwidth estimate.

Buy for the model, not the AI sticker

A model’s parameter count is only the beginning. Quantization changes the weight size, while context length and simultaneous conversations add working memory. Your operating system and inference runtime need room too.

Sizes are Ollama’s published downloads (checked October 25, 2026); add context and runtime memory on top. The first three rows use Qwen2.5; the last uses Llama3.3. Q4 means Q4_K_M, and Q8 means Q8_0. The hardware column assumes Q4 with a modest single-request context.

Class	Q4	Q8	Q4 route
7B	4.7GB	8.1GB	16GB GPU
14B	9.0GB	16GB	16GB GPU
32B	20GB	35GB	24GB GPU or unified
70B	43GB	75GB	128GB unified

How much VRAM for 70B? The Q4 example’s weights alone exceed a 24GB card. Start looking at a 48GB-class GPU or a larger unified-memory machine, then budget context and runtime memory. The 128GB EVO-X2 gives this shortlist the capacity to pursue that workload; its GPU allocation still has to accommodate it.

For perspective, the 43GB file is about 40.0GiB, and the 20GB file about 18.6GiB. Use our Ollama context and VRAM guide before choosing a long context or several parallel requests.

The desktop pick: ASUS RTX 5060 Ti 16GB

Choose the ASUS DUAL-RTX5060TI-O16G when your goal is a useful everyday model and a straightforward NVIDIA software route. Its maker specifications give you 16GB GDDR7, a 128-bit interface and a 28Gbps memory data rate. That works out to 448GB/s theoretical memory bandwidth.

It is a card, not a complete computer. You need the host, case and power supply around it. This model occupies 2.5 slots, measures 229 × 120 × 50mm and takes one eight-pin power connector. ASUS recommends a 550W system supply for its stated configuration; use our two-GPU PSU guide if your plan includes another card rather than treating that recommendation as a whole-build budget for anything you assemble.

The desktop route also lets you replace the GPU later. For 32B Q4, a 24GB card gives you a more useful weight-and-context envelope than this 16GB pick. For 70B Q4, moving from 16GB to 24GB still leaves the weights too large for one card.

The large-memory pick: GMKtec EVO-X2 128GB / 2TB

The linked configuration is the Ryzen AI Max+ 395 EVO-X2 with 128GB RAM and a 2TB NVMe SSD. GMKtec’s product page and specification graphics identify onboard LPDDR5X-8000 memory, Radeon 8060S graphics, two M.2 2280 storage positions and three cooling fans. Buy the memory configuration you want at the start; these are onboard chips, not a pair of desktop DIMMs to change later.

Its advantage is the large shared pool. You can choose a model because it is useful, rather than immediately ruling it out at the 16GB or 24GB boundary. That makes it the fit for experimenting with 32B Q8 or 70B Q4, alongside the smaller models.

128GB unified memory means system memory shared by the CPU and GPU, not 128GB of dedicated VRAM. Leave room for the OS and runtime, establish the GPU’s available allocation, and start with one conversation at a modest context. Then increase the context while watching what the runtime actually loads. The Ollama context documentation explains the extra memory cost and the ollama ps processor/context readout.

You’re buying room, not speed.

What about a Mac mini or Studio?

If you already use macOS, Metal is a supported Ollama route. Apple’s M4 Pro Mac mini specifications list 273GB/s memory bandwidth and configurations up to 64GB unified memory. That larger configuration is a more sensible starting point for a 43GB weight file than the base 24GB machine.

Keep the exact RAM configuration central to the decision: “Mac mini” is not a memory capacity. More memory also does not remove the OS, GPU-allocation and KV-cache requirements. Choose the memory capacity first, then compare the chip and storage configuration.

Why bandwidth changes token speed

For one-at-a-time generation with a dense model, repeatedly streaming the weights can be the limiting job. A useful first estimate is:

Ideal weight-stream token rate ≈ peak memory bandwidth ÷ weight size.

The inputs below use ASUS’s memory rate/interface calculation and the 256GB/s specification for AMD’s 128GB Ryzen AI Max+ 395 reference platform in its developer documentation.

Idealised example	Calculation	Estimated weight-stream ceiling
RTX 5060 Ti, 14B Q4	448GB/s ÷ 9GB	49.8 tokens/s
Ryzen AI Max+ 395 platform, 14B Q4	256GB/s ÷ 9GB	28.4 tokens/s
Same AMD platform, 32B Q4	256GB/s ÷ 20GB	12.8 tokens/s
Same AMD platform, 70B Q4	256GB/s ÷ 43GB	6.0 tokens/s

These are computed estimates, not benchmarks. Real speed is lower because computation, KV-cache traffic and scheduling use time and bandwidth too.

Use the calculation to understand the trade: more capacity can let you run a larger model without making each token arrive faster. For the timing that you actually feel, our Ollama tokens-per-second guide separates model loading, prompt work and output generation.

What we measured on the bench

Our RTX 3060 Ti 8GB power-limit sweep shows why software settings matter after the model fits. Measured September 25, 2026: Qwen2.5 7B Q4_K_M on Ollama 0.34.2, one warm-up and three 512-token runs per cap, fixed seed and temperature zero. GPU driver telemetry was sampled every 250ms; the bench notes retain the averaging method.

GPU cap	Measured generation	Average GPU draw
200W, stock cap	77.8 tokens/s	193.4W
150W cap	75.5 tokens/s	145.4W

That is about 3% less generation speed for 25% less GPU draw. Follow the NVIDIA power-limit guide to run the same kind of comparison on your hardware.

Want to combine cards? Our two-GPU B450 build records that route. Cards remain separate memory devices, so model splitting and any CPU offload matter; two cards do not become one universally interchangeable block of VRAM.

Idle power and noise: measure the whole box

Our separate card-comparison session recorded 16.5W idle board power for the 3060 Ti, from the driver. Apple’s published power table gives 5W idle for its M4 Pro Mac mini with 64GB RAM and an 8TB SSD, with Finder open and default power settings—whole computer versus card only.

GMKtec’s reviewed product specification does not supply a comparable wall-idle figure. Put the complete machine on a wall meter, let model activity settle, and record idle both before and after loading the model. Repeat the same prompt workload for load power. Our power-meter guide explains the tool choice.

A small case is not automatically silent: the EVO-X2 has three cooling fans. With a desktop you can choose the case and cooling around the card. Listen during sustained generation as well as at idle, with the machines in the same position and at the same distance.

If you are expanding a desktop for CPU inference or partial GPU offload, our 96GB DDR5 kit guide matches two-DIMM kits to platform checks and calculates room for 32B/70B Q4 models. System RAM and discrete VRAM remain separate budgets.

Choose the backend before the checkout

Ollama’s current GPU documentation lists the RTX 5060 Ti on its NVIDIA path and Ryzen AI Max+ 395 under Linux ROCm support. Its Windows ROCm table does not list the 395. Vulkan supplies an additional Windows/Linux GPU route; on an AMD Windows machine, establish that route rather than assuming a Linux ROCm listing carries across.

Apple uses Metal. If you prefer llama.cpp directly, its official build guide covers CUDA, HIP, Vulkan and Metal. Choose an OS/backend combination that names your hardware; a big NPU TOPS badge does not answer that question.

When the machine arrives, run the exact model and context you bought it for. Check ollama ps for residency, note load and generation timings, and try your expected concurrency—the Ollama FAQ explains its memory implications. Our mini-PC guide covers the smaller general-purpose hosts; for a local-LLM box, make model fit the first filter.

Buy the 16GB NVIDIA card for the smaller-model desktop. Buy the 128GB EVO-X2 for the larger-model playground. Pick the model you want to use, leave it breathing room, and measure the answer speed you actually care about.