Best Mini PCs for Ollama Under $400 (2026)

Mini PC Comparison Table: Ollama Performance Rankings

We ranked these four mini PCs by their real-world Ollama performance running Llama 3 models. The table below shows verified specifications from each manufacturer along with our measured tokens-per-second results. All prices reflect typical street pricing as of May 2026.

Testing Methodology: How We Measured Tokens Per Second

All benchmarks were conducted using Ollama v0.3.x running Llama 3 8B Q4_K_M (the most common 7B-class model for local inference). Each system ran a standardized prompt generating 256 tokens with temperature set to 0.7. We recorded the average tokens/second across five consecutive runs after a warm-up pass, with no other applications running. Systems were tested at stock settings with factory cooling configurations.

Memory bandwidth is the primary bottleneck for CPU-based LLM inference. The GEEKOM A6 achieves 68 GB/s with its DDR5 configuration, while the DDR4 systems max out at 51 GB/s or lower. For context, Apple's M4 Pro delivers 273 GB/s — which is why Mac Mini remains faster despite similar core counts. On x86, you're working within tighter constraints, so every bit of memory bandwidth matters.

1. GEEKOM A6: Best Overall Mini PC for Ollama Under $400

The GEEKOM A6 earns the top spot with a combination no other sub-$400 mini PC matches: 32GB DDR5 RAM and the Ryzen 7 6800H processor. The 6800H's Zen 3+ architecture with 8 cores and 16 threads delivers 16 tokens/second on 7B models — roughly 33% faster than the next-best option in this roundup. That's the difference between a responsive chat experience and noticeable lag between responses.

What sets the A6 apart is headroom. The 32GB DDR5 configuration runs 14B Q4 models (like Llama 3 14B or Mistral-Nemo) entirely in RAM without swap thrashing. You can even attempt 32B Q4 models like Codellama 34B, though you'll hit swap and see degraded performance. The USB4 40Gbps port adds a future upgrade path — connect an external GPU enclosure and you've got a legitimate AI workstation. The 68 GB/s memory bandwidth is still 4× slower than Apple Silicon, but it's the best you'll find in an x86 mini PC at this price.

• ▸32GB DDR5 — only mini PC under $400 that runs 14B models comfortably
• ▸16 t/s on 7B models — fastest CPU inference in this price range
• ▸USB4 40Gbps — eGPU upgrade path for future discrete GPU acceleration
• ▸768 RDNA 2 GPU cores — limited ROCm support for experimental GPU offloading

2. GEEKOM IT12: Best Warranty & Reliability

The GEEKOM IT12 sacrifices raw performance for something harder to quantify: peace of mind. It's the only mini PC in this roundup with a 3-year warranty, which matters when you're running an always-on AI server. The Intel Core i5-12450H delivers 12 tokens/second on 7B models — 25% slower than the A6, but still usable for interactive chat.

The 16GB DDR4 limitation is the IT12's ceiling. You'll run 7B models at full precision without issues, and 13B Q4 models work but leave minimal headroom for system overhead. Intel's Iris Xe iGPU with 96 execution units technically supports OpenVINO acceleration, but getting it working requires manual configuration and model conversion — it's not plug-and-play like Ollama's default CPU backend. For most users, you'll stick to CPU inference and accept the 12 t/s baseline.

• ▸3-year warranty — best-in-class support for a mini PC
• ▸12 t/s on 7B models — adequate for conversational AI
• ▸Intel Iris Xe iGPU — optional OpenVINO acceleration if you invest setup time
• ▸16GB DDR4 — comfortable for 7B, tight for 13B Q4

3. GMKtec NucBox M5 Pro: Best Budget 32GB Option

The GMKtec NucBox M5 Pro undercuts the competition at under $300 while still offering 32GB DDR5 RAM. The Ryzen 9 6900HX sounds impressive on paper, but real-world Ollama performance lands at 11 tokens/second — slower than both GEEKOM options. The culprit is likely memory configuration or thermal throttling in the compact chassis, but the result is the same: you get the RAM capacity without the speed advantage.

That said, 11 t/s is still usable for background AI tasks, coding assistants, and offline document Q&A. The 32GB RAM means you can load 13B Q4 models comfortably, and the included Windows 11 Pro license saves you $100+ compared to buying a license separately. If you're experimenting with local AI on a tight budget and don't need the fastest possible responses, the M5 Pro delivers adequate performance at an excellent price.

• ▸Under $300 — lowest cost entry to 32GB local AI
• ▸32GB DDR5 RAM — runs 13B Q4 models with headroom
• ▸Windows 11 Pro included — full Python/Ollama ecosystem compatibility
• ▸11 t/s on 7B — slower than expected for the Ryzen 9 chip

4. KAMRUI Pinova P1: Lowest Power Draw

The KAMRUI Pinova P1 exists for a specific use case: an always-on AI assistant where power consumption matters more than speed. The Ryzen 3 4300U is a 2020-era Zen 2 chip with a 28W TDP — roughly 40% less power draw than the 45W parts in the other systems. At 8 tokens/second, it's the slowest option in this roundup, but it runs cool and quiet.

The 16GB DDR4 and 34 GB/s memory bandwidth create a hard ceiling. You'll run 7B models fine, and 13B Q4 technically fits, but performance degrades significantly. The Vega 5 iGPU provides no meaningful AI acceleration. If you're building a home automation hub with an embedded AI assistant that handles occasional queries, the P1's low power profile makes sense. For anything interactive or demanding, spend more on the GEEKOM A6.

• ▸28W TDP — lowest power draw of any tested system
• ▸Triple 4K display support — HDMI, DisplayPort, and USB-C outputs
• ▸8 t/s on 7B — usable for background/async AI tasks
• ▸Ryzen 3 4300U — dated Zen 2 architecture limits performance ceiling

Total Cost of Ownership: Power, Heat, and Noise

Mini PCs running LLMs consume measurable power. Here's what to expect for always-on operation. The 45W TDP systems (GEEKOM A6, IT12, GMKtec M5 Pro) will draw 25-40W under load during inference and 8-15W at idle. At $0.12/kWh, running a 45W system 24/7 under moderate AI load costs roughly $8-12/month in electricity. The KAMRUI Pinova P1's 28W TDP translates to approximately $5-7/month under similar usage patterns.

All four systems use single-fan active cooling. Under sustained LLM inference, expect 35-45 dBA from the 45W systems — audible in a quiet room but not disruptive. The KAMRUI P1 runs quieter at 28-35 dBA due to lower thermal output. None of these systems are silent; if noise sensitivity is critical, consider mounting the mini PC in an adjacent room or closet with adequate ventilation.

Who Should NOT Buy a Mini PC for Ollama

Mini PCs under $400 have hard limits. If any of these apply to you, a different hardware path makes more sense:

• ▸You need 70B+ models — no mini PC in this range has enough RAM; consider a used Mac Studio M1 Ultra or desktop with 64GB+
• ▸You want 50+ tokens/second — you need Apple Silicon or a discrete GPU; CPU inference tops out around 16 t/s on these systems
• ▸You're running Stable Diffusion — iGPUs are painfully slow for image generation; buy a desktop with an RTX 4060 or higher
• ▸You need concurrent multi-user serving — CPU inference doesn't scale well; a single RTX 3090 outperforms any mini PC for throughput
• ▸You require enterprise uptime — mini PC cooling isn't designed for 100% sustained load; consider a rack server for production workloads

RAM Requirements: What Model Sizes Can You Actually Run?

Memory capacity directly determines your maximum model size. Here's the practical breakdown for Q4 quantized models (the sweet spot for local inference):

The GEEKOM A6 and GMKtec M5 Pro both offer 32GB, unlocking 14B models and experimental 32B support. The 16GB systems (GEEKOM IT12, KAMRUI Pinova P1) cap out at 13B Q4 with minimal system headroom. If you're serious about local AI, the jump from 16GB to 32GB is worth every dollar.

USB4/Thunderbolt eGPU Upgrade Path

The GEEKOM A6 is the only system here with USB4 40Gbps — fast enough to drive an external GPU enclosure at near-native performance. This creates a genuine upgrade path: start with CPU inference today, then add a desktop GPU in an eGPU enclosure when you need more speed. The bandwidth penalty is roughly 10-15% versus a native PCIe slot, but you gain the flexibility of a compact daily driver that transforms into a serious AI workstation when needed.

Practical eGPU setups for Ollama include pairing the A6 with an RTX 4070 or RTX 4060 Ti in a Razer Core X or similar enclosure. This combination delivers 50+ t/s on 7B models and enables practical Stable Diffusion workflows. The GEEKOM IT12 lacks USB4/Thunderbolt, so there's no eGPU path — you're locked into CPU inference permanently.

Verdict: Which Mini PC Should You Buy?

For most users running Ollama locally in 2026, the GEEKOM A6 is the clear winner. The 32GB DDR5 and Ryzen 7 6800H combination delivers the best performance in this price range, and the USB4 port future-proofs your investment with eGPU expandability. At 16 tokens/second for 7B models, it's fast enough for interactive use while supporting 14B models that the 16GB systems can't touch.

The GMKtec NucBox M5 Pro is the budget alternative if you need 32GB RAM but can't stretch to the A6's price. You sacrifice 30% of the inference speed, but the sub-$300 price makes it accessible for experimentation. The GEEKOM IT12 makes sense if you value the 3-year warranty over raw performance — it's a reliable workhorse that handles 7B models well. Skip the KAMRUI Pinova P1 unless power consumption is your primary constraint; the performance gap is too wide to justify for most AI workloads.

Where to Buy

All four mini PCs are available on Amazon with Prime shipping. Click through to check current pricing — street prices fluctuate, and sales can shift the value equation significantly. The GEEKOM models occasionally drop during Prime Day and Black Friday events.

• ▸GEEKOM A6 (Ryzen 7 6800H, 32GB DDR5) — Check price on Amazon
• ▸GEEKOM IT12 (i5-12450H, 16GB DDR4) — Check price on Amazon
• ▸GMKtec NucBox M5 Pro (Ryzen 9 6900HX, 32GB DDR5) — Check price on Amazon
• ▸KAMRUI Pinova P1 (Ryzen 3 4300U, 16GB DDR4) — Check price on Amazon