Best Mini PC for Gemma 4 in 2026: $300 to $2,000 Picks

A viral HowToGeek article about running Gemma on a $300 mini PC has a lot of people asking the same question: which mini PC should I actually buy to run Gemma 4? This guide answers it with real, sourced specs and prices across three budgets — no invented numbers, no affiliate links.

Quick Answer

If you just want one recommendation: get a Mac Mini M4 with 16 GB unified memory. At $799 (Apple, current price after Apple's May 2026 hike from $599 — see MacRumors), it runs Gemma 4 12B at Q4 (6.7 GB) comfortably through Metal/MLX acceleration, is silent, sips power, and holds its resale value far better than a Windows mini PC. If $799 is out of reach, a 32 GB Ryzen mini PC around $300–400 gets you E2B/E4B comfortably and 12B usably, just much slower on CPU alone.

Comparison Table

Tier	Machine	Price	Memory	Best Gemma 4 Fit
Budget	Peladn W04 (Ryzen 5 7640HS)	~$300	32 GB LPDDR5	E2B/E4B comfortable, 12B usable, slow
Budget	GMKtec NucBox K8 Plus (Ryzen 7 8845HS)	~$390–520	32 GB DDR5 (up to 96 GB)	Same as above, more storage/upgrade room
Mid	Mac Mini M4 (16 GB)	$799	16 GB unified	12B (Q4) — the sweet spot
Mid	Beelink SER9 Pro (Ryzen AI 9 HX 370)	~$799–999	32 GB LPDDR5X	12B fast, 26B A4B fits
Performance	Mac Mini M4 Pro (24 GB)	$1,599	24 GB unified	26B A4B comfortable
Performance	GMKtec EVO-X2 (Ryzen AI Max+ 395)	~$1,999–2,199	128 GB LPDDR5X	26B A4B and 31B, big headroom

Sources for every price and spec are linked in the sections below.

What Specs Actually Matter

Before the picks, a quick reality check on what to look for — because mini PC marketing loves to bury the number that matters most.

RAM beats everything else. Unlike gaming, where GPU and CPU clock speeds dominate, running an LLM locally is a memory-capacity problem first and a speed problem second. If the model's weights don't fit in memory, it doesn't run — full stop. Our hardware requirements guide breaks down exact memory needs per Gemma 4 model; the short version, from Google's official specs:

Model	Q4 (4-bit)	SFP8 (8-bit)	BF16 (16-bit)
E4B	5 GB	7.5 GB	15 GB
12B	6.7 GB	13.4 GB	26.7 GB
26B A4B (MoE)	15.6 GB	25 GB	48 GB
31B	17.4 GB	30.4 GB	58.3 GB

Add 30–50% on top for KV cache and OS overhead, and for CPU-only inference (no dedicated GPU), plan for roughly double the weight size in free system RAM.

Unified memory is a mini PC's secret weapon. Apple Silicon and AMD's Ryzen AI Max chips share one memory pool between CPU and GPU/NPU. That means a 32 GB mini PC can dedicate most of its RAM to a model, instead of being capped by a separate 8–16 GB VRAM pool the way a typical gaming desktop is. It's the same principle covered in our hardware requirements guide — it's just usually a bigger relative advantage in a mini PC, since these machines rarely have a discrete GPU at all.

iGPU vs. CPU-only matters for speed, not for whether it runs. Most budget mini PCs have no discrete GPU — inference either runs on the integrated graphics (if the runtime supports it) or falls back to the CPU. Either way, it's slower than a discrete NVIDIA card, but it works.

Budget Tier: ~$300

This is the tier HowToGeek's article put on the map. The author ran Gemma 3 12B on a Peladn W04 — Ryzen 5 7640HS, 32 GB LPDDR5, 250 GB SSD, Radeon 760M integrated graphics, "just under $300" — and reported 7.1 tokens/second on the CPU alone, with typical replies taking 5–25 seconds (HowToGeek).

That's Gemma 3, not Gemma 4, but it's the closest real-world data point for this exact class of hardware, and it's a reasonable proxy: Gemma 4's 12B is a similarly-sized dense model. Independent CPU-only tests of Gemma 4 specifically back this up — TerminalBytes benchmarked a $150 used i5-8500 desktop (32 GB RAM, no GPU) and got ~7 tokens/second on Gemma 4's 26B A4B (MoE) model at Q4, versus "low single digits" for the dense 12B on the same box. That's not a typo — the MoE model, despite a larger total footprint (15.6 GB vs. 6.7 GB), only activates ~3.8B parameters per token, so it can out-run the fully-active 12B dense model on CPU. It's the same MoE advantage explained in our hardware requirements guide.

Named picks for this tier:

• Peladn W04 — Ryzen 5 7640HS, 32 GB LPDDR5, 250 GB SSD, ~$300. The exact machine from the HowToGeek piece. (HowToGeek)
• GMKtec NucBox K8 Plus — Ryzen 7 8845HS (8C/16T), 32 GB DDR5-5600 on socketed SO-DIMM slots (upgradeable to 64–96 GB), 1 TB SSD, listed from ~$389 on sale, ~$500–522 at typical retail. (Team Pandory review, Notebookcheck)

Realistic experience: E2B and E4B run at usable, real-time speeds — this class of hardware was designed around exactly that model size. The 12B dense model runs, but expect low-single-digit to ~7 tokens/second depending on the exact chip; fine for editing, summarizing, and explaining code (the tasks HowToGeek highlighted), not fine for long back-and-forth chat. The 26B A4B MoE model technically fits in 32 GB and, per TerminalBytes, can actually be as fast or faster than the 12B thanks to its lower active-parameter count — worth trying before assuming you need more RAM.

Mid Tier: ~$500–800

This is where the experience stops feeling like a compromise.

• Mac Mini M4, 16 GB unified memory — $799 at Apple as of mid-2026. Apple discontinued the $599/256 GB base configuration in May 2026 and folded everyone into the $799/512 GB tier, citing memory chip shortages tied to AI data center demand (MacRumors). It's a real price jump from the machine's original $599 launch price, worth knowing about since a lot of older buying guides still quote $599. What you get for it: Gemma 4 12B (6.7 GB at Q4) fits with room to spare, runs through Apple's Metal/MLX acceleration rather than raw CPU, and Google's own 12B announcement specifically calls out 16 GB machines as the target (Google).
• Beelink SER9 Pro, Ryzen AI 9 HX 370 — 12C/24T, 32 GB LPDDR5X, 1 TB SSD. Pricing varies by retailer and sale: launched around $799 (Minixpc), with Tom's Hardware logging a $999 sale price down from a $1,249 list (Tom's Hardware). Its Radeon 890M iGPU and 32 GB unified-style memory pool run 12B fast and comfortably fit 26B A4B (15.6 GB at Q4) as well.

Realistic experience: the Mac Mini M4 is the better value if 16 GB is enough for you — it's quieter, more power-efficient, and Apple Silicon's unified memory plus Metal acceleration means 12B runs noticeably faster than the CPU-only budget boxes above (Gemma 3 12B testing on M2/M3 MacBook Pro-class Apple Silicon reported 30–50 tokens/second — see Kunal Ganglani's benchmarks — a reasonable proxy for M4-class performance on the similarly-sized Gemma 4 12B). The Beelink gives you more RAM headroom for 26B A4B if you want a bigger quality jump without moving to the performance tier.

Performance Tier: ~$1,000+

For 26B A4B comfortably or the full 31B dense model, you need either a lot of fast unified memory (Apple) or a lot of cheap unified-style memory (AMD's Ryzen AI Max+ platform).

• Mac Mini M4 Pro, 24 GB unified memory — $1,599 at Apple, up from $1,399 before Apple raised M4 Pro pricing again in June 2026, also citing memory costs (MacRumors). 24 GB comfortably fits 26B A4B (15.6 GB at Q4) with room for a long context window.
• GMKtec EVO-X2, Ryzen AI Max+ 395, 128 GB LPDDR5X — pricing has moved around: GMKtec's own promotional pricing put the 128 GB/2 TB configuration at roughly $1,999, while Tom's Hardware logged a GMKtec-direct price near $2,199 by mid-June 2026, with retail listings running slightly higher. Independent testing on this exact machine found it running Qwen3-235B (a much larger MoE model) at ~11 tokens/second and Llama 3.3 70B (Q6_K) at 3.7–3.8 tokens/second. Gemma 4's 26B A4B activates far fewer parameters per token (3.8B) than either of those, so expect noticeably better throughput on this hardware — we couldn't find a Gemma-4-specific benchmark on the EVO-X2 to cite an exact number, so treat that as a reasoned expectation, not a verified figure.

Realistic experience: the Mac Mini M4 Pro is the tidier, quieter choice if 26B A4B is your ceiling. The Ryzen AI Max+ machines cost more but their 96–128 GB unified memory pools mean you're not boxed in if you later want to run something even bigger than Gemma 4 offers today — useful if you see this as infrastructure, not a one-model purchase.

Setting It Up

Picking the machine is half the job. Once it arrives:

1. Confirm exactly how much memory your specific configuration has and re-check it against our Gemma 4 hardware requirements guide — mini PC listings are notorious for burying the actual RAM config below the fold.
2. Install Ollama and pull the right model tag for your machine — our step-by-step Ollama guide covers installation, model selection by size, and the local API.
3. If you'd rather not use the terminal, LM Studio gives you a GUI model browser that will auto-suggest the right Gemma 4 size for your RAM — including the new 12B model, which it now recommends by default for 16 GB machines.

Honest Limitations

A mini PC is not a substitute for a discrete GPU, and it's worth setting expectations before you buy:

• CPU-only inference is genuinely slow. The 5–25 second response times HowToGeek reported at 7.1 tokens/second are fine for asynchronous tasks like editing or summarizing, but noticeably worse than the near-instant feel of a cloud chatbot. If you're chasing that feel, you need the GPU/NPU acceleration path (Metal on a Mac, or a Ryzen AI Max's iGPU), not CPU fallback.
• "AI-branded" laptop chips aren't the same as a discrete GPU. NPUs on Ryzen AI or Intel Core Ultra chips accelerate specific AI workloads, but most current local-LLM runtimes (Ollama, llama.cpp) lean on CPU or integrated GPU compute for text generation, not the NPU. Don't buy a mini PC purely because it says "AI" on the box — check what the inference runtime you'll actually use supports.
• Multimodal features (native audio, image input) cost more memory and compute than text alone, even though the table above only covers text weight sizes. If you plan to lean on Gemma 4 12B's native audio input, budget extra headroom beyond the bare 6.7 GB figure.
• Mini PCs can't be upgraded like a desktop. Most (Mac Mini, Beelink SER9, GMKtec EVO-X2) have soldered memory — buy the RAM configuration you'll actually need, because there's no adding more later. The GMKtec NucBox K8 Plus is a rare exception with socketed SO-DIMM slots.

Bottom Line

For most people typing "gemma ai mini pc" into Google after reading the HowToGeek piece, the honest answer is: a $300 box will run Gemma 4 12B, just slowly, and it'll run E2B/E4B just fine. If you can stretch to $799, a 16 GB Mac Mini M4 turns 12B from "usable" into "comfortable" thanks to Metal acceleration. Past that, it's a question of how much of Gemma 4's larger MoE and dense models you actually want to run locally — and whether that's worth $1,600–2,200 versus a cloud API subscription.