Best Mini PCs for Local AI in 2026: A Buyer’s Guide

Two years ago, running a capable language model at home meant a tower stuffed with two or three GPUs, a 1,000-watt power supply, and a fan profile that sounded like a hairdryer. In 2026 you can do most of the same work from a box that fits in your palm and sips power like a laptop. The catch is that the mini-PC market has fragmented into machines that look similar but behave very differently once a model is loaded.

This guide cuts through that. We compare the four classes of small-form-factor machine that actually matter for local AI right now — Apple’s Mac mini, NVIDIA’s DGX Spark, AMD’s Ryzen AI Max+ (“Strix Halo”) boxes, and Intel’s NPU-equipped mini PCs — with verified specs, current prices, and published token-per-second numbers. By the end you’ll know which one fits the models you want to run, and which spec sheet lines are marketing rather than performance.

Unified memory vs VRAM: the one concept that explains everything

Every recommendation below hinges on this distinction, so it’s worth thirty seconds.

A traditional GPU has its own dedicated VRAM. An RTX 4090 has 24GB; if your model and its context don’t fit in 24GB, it won’t run on that card, full stop. VRAM is fast — the 4090’s is about 1,008 GB/s — but there’s never much of it relative to model sizes.

Unified memory flips the trade. Apple’s M-series, NVIDIA’s GB10, and AMD’s Strix Halo all share a single pool of memory between CPU and GPU, so a 128GB machine can dedicate 96GB-plus to a model. That’s how a palm-sized box runs a 120-billion-parameter model that won’t fit on any single consumer GPU. The price you pay is bandwidth: unified LPDDR5x runs at roughly 150–310 GB/s in these machines, a fraction of discrete VRAM. And because token generation is memory-bandwidth-bound, that ceiling sets your tokens-per-second — no matter how much compute the chip claims. Hold those two numbers (capacity and bandwidth) in mind and every spec sheet below becomes readable. If you want the full GPU-side picture, see our companion piece on the best GPUs for local LLMs in 2026.

One note on the benchmark model we lean on below: gpt-oss-120B is a mixture-of-experts model with about 117B total parameters but only ~5.1B active per token. That MoE design is exactly why a quantized version fits — and runs at usable speeds — on these unified-memory boxes despite their modest bandwidth.

Apple Mac mini (M6 / M5 Pro): the default pick

The Mac mini remains the easiest recommendation for the largest number of people. Apple replaced the M4 and M4 Pro models with a new generation that went on sale on 22 September 2026: the base model now uses the M6 chip and starts at $899 (16GB unified memory, 256GB SSD), configurable to 24GB or 32GB.

The M6 has a 12-core GPU and 153 GB/s of memory bandwidth (170 GB/s in the 24GB and 32GB configurations) — fine for 8B-class models. For serious local AI you want the M5 Pro, which steps up to a 16- or 20-core GPU and, crucially, 307 GB/s of bandwidth — roughly double the M6. It starts at $1,699 with 24GB; configured with 48GB or 64GB, it holds a 32B model quantized to Q4 entirely in memory (Qwen 2.5 32B at Q4_K_M is a 19.8GB file). Apple lists the 18-core CPU, 20-core GPU, 64GB, 1TB configuration at $3,199. In llama.cpp’s community Apple Silicon scoreboard, the M5 Pro generates about 66–68 tok/s on Llama 2 7B at Q4_0.

A caveat worth knowing before you buy: 64GB is the ceiling. That is enough to hold a 70B model at Q4 (Llama 3 70B at Q4_K_M is a 42.5GB file) with only modest room for context, and token generation on a dense 70B model is slow at 307 GB/s. If you specifically need more than that, the 128GB unified-memory boxes below are the realistic path.

What sells the Mac mini isn’t peak speed — it’s the whole package. Apple lists its idle noise at 5 dBA and rates it at 155W maximum continuous power, and it is small and quiet enough to leave running as an always-on inference server on a shelf. Software support via Ollama, LM Studio, and Apple’s MLX framework is excellent. If you’re new to local models, our complete guide to Ollama walks through getting a model running on exactly this kind of machine.

NVIDIA DGX Spark: 128GB and a CUDA stack, at a price

The DGX Spark (unveiled as “Project DIGITS” at CES 2025, renamed DGX Spark at GTC in March 2025, and shipping October 15, 2025) is NVIDIA’s bid to put a “personal AI supercomputer” on your desk. It pairs a GB10 Grace Blackwell superchip — a 20-core Arm CPU (10× Cortex-X925 + 10× Cortex-A725) plus a Blackwell GPU — with 128GB of coherent unified LPDDR5x, a 4TB self-encrypting SSD, and a ConnectX-7 200 Gbps NIC for linking two units. NVIDIA rates it at up to 1 petaflop of FP4 AI performance, and per NVIDIA it can run inference on models up to ~200B parameters, or fine-tune up to ~70B. Power is supplied by a 240W adapter.

Here’s the honest part. The Spark is a prompt-processing monster: on gpt-oss-120B it pushes about 1,723 tok/s of prefill in standardized testing, in the league of a triple-RTX-3090 rig. But token generation lands at just ~38.6 tok/s in the same comparison, because the GB10’s memory bandwidth is only 273 GB/s — a little below the new Mac mini M5 Pro’s 307 GB/s, and the binding constraint during the memory-bound decode phase. Heavily optimized inference stacks (vLLM, SGLang, NVIDIA’s own TensorRT-LLM) have reportedly pushed single-unit gpt-oss-120B generation up toward 50–60 tok/s with the right config, but the bandwidth ceiling that keeps it well below a multi-GPU rig is physics, not software.

Then there’s price. The Spark launched at $3,999 and rose to $4,699 in February 2026 amid memory-supply constraints — an 18% jump NVIDIA attributed to worldwide memory-supply constraints. That’s roughly $37 per GB of memory — and for pure generation throughput, a trio of used RTX 3090s reportedly costs less and runs several times faster. The Spark earns its keep if you specifically need the CUDA/NVIDIA software stack, NVFP4, or the 200B-parameter inference headroom in a 240W box. We dig deeper in our DGX Spark / Project DIGITS review and the head-to-head DGX Spark vs Mac Studio comparison.

AMD Ryzen AI Max+ 395 (Strix Halo): the value play

AMD’s Strix Halo is the surprise of this generation. The flagship Ryzen AI Max+ 395 packs 16 Zen 5 cores, a 40-compute-unit RDNA 3.5 GPU (the Radeon 8060S), and a 50-TOPS XDNA 2 NPU. Paired with up to 128GB of LPDDR5X, of which up to 96GB can be handed to the GPU, it does the same unified-memory trick as the Spark — run a 120B model that no single consumer GPU can hold — at a fraction of the cost.

The trade is bandwidth and prompt processing. Strix Halo’s memory tops out around 256 GB/s, and on gpt-oss-120B it manages only ~340 tok/s of prefill versus the Spark’s 1,723. But here’s the kicker: token generation is ~34 tok/s, within a whisker of the Spark’s 38. For chat-style workloads where you generate more than you ingest, the gap is small. Dense 70B models are another story: at Q4 every generated token has to read the full ~42.5GB of weights through that ~256 GB/s bus, so expect single-digit tok/s.

What makes it compelling is price and choice. Entry pricing has climbed with the 2026 RAM shortage: 64GB versions of the GMKtec EVO-X2 sell from around $1,500, while the 128GB EVO-X2 now runs closer to $2,200. Framework’s repairable Desktop starts around $1,639 (barebones, Ryzen AI Max+ 395, 64GB) before storage and OS, and Corsair’s AI Workstation 300 launched near $2,000 but has swung well past that during the shortage. Software is the catch — ROCm and llama.cpp work well, but the ecosystem is rougher than Apple’s or NVIDIA’s, and Windows AI features lean on the NPU rather than the big GPU.

Intel mini PCs: small models and NPU offload

Intel’s mini PCs occupy a different tier, and it’s important not to mis-buy here. Current Arrow Lake-H chips like the Core Ultra 9 285H pair an Arc iGPU with a 13-TOPS NPU, for up to roughly 99 TOPS of platform AI compute when you count the CPU and GPU; Intel’s IPEX-LLM stack brings Ollama and llama.cpp to its iGPU and NPU. The bigger 2026 step is Panther Lake (Core Ultra Series 3), launched at CES 2026, which pairs a 50-TOPS NPU with a much stronger GPU for up to ~180 platform TOPS — though it remains a laptop/mobile-class platform, not a desktop big-model machine.

But none of these are unified-memory big-model machines. With standard DDR5 (typically ~120 GB/s dual-channel) and no 96GB-to-GPU allocation, an Intel mini PC is the right tool for 3B–8B models, on-device assistants, transcription, and NPU-accelerated background tasks — not for running a 70B model. If your workload is “a quantized 8B model and some Windows AI features,” an Intel box is cheap and power-efficient. If it’s “the biggest model I can fit,” look at the unified-memory machines above. The NPU-versus-GPU trade-off here is its own topic, covered in our NPU vs GPU for AI breakdown.

The comparison table

Generation figures below are for gpt-oss-120B (token generation / prompt processing) from standardized llama.cpp-style testing where both fit; smaller-model notes are given otherwise. Prices are USD — Apple’s are current list prices, the rest mid-2026 street prices — and move with the ongoing memory shortage.

Machine	Unified memory	Bandwidth	Realistic model ceiling	Gen / prefill (120B)	Power	Price (2026)
Mac mini M6 (base)	16–32GB	153–170 GB/s	~8–14B (Q4)	n/a	up to 155W (rated)	$899+
Mac mini M5 Pro	24–64GB	307 GB/s	~32B (Q4); 70B at Q4 only with 64GB, tight	n/a	up to 155W (rated)	$1,699+ ($3,199 with 64GB)
AMD Strix Halo (Ryzen AI Max+ 395)	up to 128GB	~256 GB/s	~120B (Q4 MoE)	34 / 340 tok/s	80–120W	$1,500–3,000+
NVIDIA DGX Spark	128GB	273 GB/s	~200B (inference)	39 / 1,723 tok/s	~240W max	$3,999–4,699
Intel Arrow Lake-H mini PC	DDR5 (no big GPU pool)	~120 GB/s	~8B (Q4)	n/a	~65W	$600–1,200

Apple replaced the M4 and M4 Pro Mac mini with the M6 and M5 Pro models on 22 September 2026; the Mac mini figures above come from Apple’s current spec sheet and list prices.

For reference, a Mac Studio M3 Ultra runs about 819 GB/s of bandwidth (and decodes the same 120B model around 70 tok/s), while a triple-3090 rig hits ~124 tok/s decode — both well outside mini-PC territory, and a reminder of what you trade away for the small footprint. If you’re weighing a larger Apple box, the current Mac Studio (on sale since September 2026) comes with M5 Max (up to 128GB, up to 614 GB/s) or M5 Ultra (up to 512GB, 1.2 TB/s); our DGX Spark vs Mac Studio comparison covers that step up.

Recommendations by use case

Most people / silent always-on assistant: Mac mini M5 Pro with 48GB or 64GB. The best balance of capability, low noise, small size, and a mature software stack. Drop to the M6 model if you only need 8B models.

Maximum model size on a budget: an AMD Strix Halo box (GMKtec EVO-X2, Framework Desktop, or Corsair AI Workstation 300). 128GB lets you load 70B–120B models the Mac mini can’t touch, at generation speeds that nearly match the far pricier Spark — and even with shortage-inflated pricing, it’s well below the Spark.

CUDA development / NVIDIA workflow / heavy prompt processing: DGX Spark. You’re paying a premium for the NVIDIA stack, NVFP4, ConnectX clustering, and best-in-class prefill — justified only if those specifically matter to you.

Small on-device models and NPU tasks: an Intel Arrow Lake or Panther Lake mini PC. Cheap, efficient, and right-sized for 8B-class work and Windows AI features.

FAQ

What’s the best mini PC for running local LLMs in 2026?

For most users it’s the Mac mini M5 Pro with 48GB or 64GB — quiet, low-power, and capable up to ~32B models. If you need to run 70B+ models, an AMD Strix Halo box with 128GB is the value choice, and the NVIDIA DGX Spark is the premium CUDA option.

How much RAM do I need to run a 70B-parameter model?

A 70B model quantized to Q4 needs roughly 40–48GB just for weights, plus headroom for context. In practice you want a 64GB machine at minimum, and 128GB to run it comfortably with a large context window. A 64GB Mac mini M5 Pro can hold a 70B model at Q4 with little room for context; for comfortable 70B work, the 128GB unified-memory boxes are the better fit.

Why is the NVIDIA DGX Spark slow at generating tokens despite costing $4,000+?

Because token generation is limited by memory bandwidth, and the Spark’s 273 GB/s is modest — a little below the Mac mini M5 Pro’s 307 GB/s. Its strength is prompt processing (about 1,723 tok/s on a 120B model) and 128GB of capacity, not raw generation speed, where standardized tests put it around 38 tok/s (optimized stacks can reach ~50–60).

Is unified memory as good as a dedicated GPU’s VRAM?

It’s a trade. Unified memory gives you far more capacity (up to 128GB) so you can run models that won’t fit on any single consumer GPU, but at much lower bandwidth than VRAM. For large models that won’t fit otherwise, it’s the only practical option; for smaller models a discrete GPU is faster.

Can a Mac mini run a 70B model?

Only just. The top Mac mini M5 Pro configuration has 64GB, enough to hold a 70B model at Q4 (Llama 3 70B at Q4_K_M is a 42.5GB file) with little room for context, and generation is slow at 307 GB/s. Practical headroom tops out around 32B at Q4. For regular 70B work, step up to a 128GB machine like a Strix Halo box, a Mac Studio, or the DGX Spark.

Are AMD Strix Halo mini PCs good for AI, or is the software too rough?

They’re genuinely capable — 128GB of memory and generation speeds near the DGX Spark for a fraction of the price. The caveat is software: ROCm and llama.cpp work but are less polished than Apple’s MLX or NVIDIA’s CUDA, and prompt processing is weak. If you’re comfortable with some setup, the value is excellent.

How much power and noise should I expect from these machines?

The Mac mini is the quietest: Apple lists 5 dBA at idle and a 155W maximum continuous power rating. Strix Halo boxes draw 80–120W with audible but modest fans. The DGX Spark ships with a 240W power adapter. All are dramatically quieter and lower-power than a multi-GPU desktop, which can pull 300–450W or more.

Bottom line

The mini-PC era for local AI is real, but the marketing oversells it in one specific way: these machines win on size, silence, power, and large unified memory — not on raw speed. No box here beats a multi-GPU desktop on tokens per second, and you shouldn’t buy one expecting that.

Pick by the model size you actually run. For 8B–32B models with the least fuss, the Mac mini M5 Pro is the easy call and the one we’d recommend to most readers. To run 70B–120B models without a tower, an AMD Strix Halo box delivers the best capability per dollar, with the DGX Spark reserved for those who specifically need NVIDIA’s stack and prompt-processing muscle. And if your needs stop at 8B models, an Intel mini PC will do the job for less. Match the memory to the model, read the bandwidth line, and ignore the petaflop on the box.