5 Best Mini PCs for Running Ollama Locally in 2026

Why Run AI Models Locally?

I’ve been running Ollama on local hardware for over a year now, and it’s changed how I work with AI. No API costs, no privacy concerns about sending your data to a cloud server, and no connectivity dependency when your internet goes out at the worst possible time. Your models are just there, instant, private, and always available.

The challenge is picking the right hardware. Mini PCs have become the sweet spot for home Ollama setups: they’re compact, quiet, energy-efficient, and increasingly powerful. But not all mini PCs are created equal for this use case.

Here’s what actually matters when choosing a mini PC for Ollama:

• RAM is king. Ollama loads models entirely into memory. A 7B model needs roughly 5-6 GB of RAM; a 13B model needs 10-12 GB; a 34B model needs around 20 GB. Get more RAM than you think you need.
• CPU performance matters for throughput. Faster inference means less waiting between tokens. A CPU with high single-thread clock speeds and good IPC wins here.
• Thermals and sustained TDP. A mini PC that throttles under sustained load will give you painfully slow generation. Look for models with good cooling or configurable TDP.
• Storage speed. Models load from disk before sitting in RAM. An NVMe SSD makes model switching much faster.

Quick pick: If you just want the best balance of price and performance, grab the Beelink SER7. For the best efficiency and Apple ecosystem integration, the Mac Mini M4 is hard to beat. On a tight budget, the Beelink Mini S12 Pro is a surprising workhorse.

Quick Comparison Table

1. Beelink Mini S12 Pro: Best Budget Ollama Starter

Best for: Getting started with Ollama on a tight budget

Don’t underestimate the Beelink Mini S12 Pro. For under $200, you get Intel’s N100 processor (4 cores, up to 3.4 GHz), 16 GB of LPDDR5 RAM, and a 500 GB NVMe SSD. That’s a legitimate Ollama machine at a price that’s hard to argue with.

The N100 is an Alder Lake-N chip with a 6W TDP, which means this thing barely sips power. I’ve run it for weeks without it ever breaking a sweat thermally. The tradeoff is raw speed: don’t expect lightning-fast token generation. For llama3.2:3b you’ll get a comfortable 20-30 tokens per second, and for mistral:7b expect 10-15 tokens per second. That’s totally usable for local chat and experimentation.

With 16 GB of RAM, you can comfortably run 7B parameter models without quantization issues. The 500 GB SSD gives you plenty of room to store several models locally. This is a fantastic option for a dedicated Ollama box that runs 24/7 for almost nothing in electricity costs.

Where it struggles: anything above 7B will feel sluggish, and the integrated Intel UHD Graphics doesn’t offer meaningful GPU acceleration for inference. But as a budget entry point into local AI? It’s hard to beat.

Verdict: The Beelink Mini S12 Pro is the best Ollama starter kit for the price. Buy it if you want to experiment with local AI without spending much, or as a dedicated low-power always-on inference box for 3B-7B models.

2. MINISFORUM UM773 Lite: Best Mid-Range Choice

Best for: Running 7B-13B models with room to grow

The MINISFORUM UM773 Lite is where things get serious. It packs AMD’s Ryzen 7 7745HX (8 cores, 16 threads, up to 5.1 GHz) into a mini PC form factor, and that processor is no joke. This is essentially a cut-down desktop CPU with serious single-thread performance, which translates directly to fast token generation.

The base configuration ships with 16 GB of DDR5 RAM, but crucially the memory is upgradeable to 64 GB via two SO-DIMM slots. That’s a huge deal for Ollama users who want to eventually run 34B or even 70B (quantized) models. The 512 GB NVMe SSD is quick enough to load models fast, though you’ll probably want an external drive if you’re hoarding models.

The AMD Radeon 780M integrated GPU (12 compute units, RDNA 3 architecture) won’t replace a discrete graphics card, but it does provide some GPGPU capability. Ollama can offload some layers to it, which helps at the margin. More importantly, the CPU performance means you’ll get 20-35 tokens per second on 7B models and a usable 10-18 tokens per second on 13B models.

Thermal management is solid. The UM773 Lite uses a well-designed cooling system that sustains its performance without throttling under extended inference workloads. It runs warm but not hot, and it’s quiet enough for a home office setup.

Verdict: The UM773 Lite is a great middle ground. Buy it in base config and upgrade RAM later when you’re ready to push bigger models. The upgrade path to 64 GB is what really makes this stand out in its price bracket.

3. Apple Mac Mini M4: Best for Efficiency and Apple Users

Best for: Apple ecosystem users who want outstanding performance per watt

The Apple Mac Mini M4 deserves a special mention for Ollama users. Unified memory architecture means that the CPU and GPU share the same high-bandwidth memory pool, and Ollama can take advantage of that in ways that traditional CPU-only inference cannot. The result is that the M4 punches well above its weight class for AI workloads.

With the base 16 GB unified memory config, the M4 Mac Mini can run 13B models smoothly and handle 27B quantized models reasonably well. The memory bandwidth on the M4 is significantly higher than what you’d find on typical x86 mini PCs at this price point, which means tokens come out faster per GB of RAM. I regularly see 25-40 tokens per second on mistral:7b and 12-20 tokens per second on llama3.1:13b, which are genuinely impressive numbers for a 10W idle machine.

Ollama on macOS also benefits from Apple’s MLX framework under the hood, and Metal GPU acceleration means a meaningful portion of inference work offloads to the GPU. The M4’s 10-core GPU handles this beautifully without the thermal drama you’d see in a comparable x86 machine.

The catch: you’re locked into whatever RAM Apple ships it with. The 16 GB base model is legitimately limiting for larger models, and the 24 GB or 32 GB configs cost significantly more. But if you’re in the Apple ecosystem and want a machine that’s also a great general-purpose desktop, this is the one to get.

Verdict: The Mac Mini M4 is the efficiency champion. If you’re already in the Apple ecosystem, this is a no-brainer. If you’re on Windows or Linux, the value proposition gets harder to justify at the base $599 price point, but the performance is genuinely excellent for Ollama.

4. Beelink SER7: Best Overall Value for Serious Ollama Use

Best for: Power users who want 13B-34B model capability without breaking the bank

The Beelink SER7 is the one I’d recommend to most people asking about Ollama hardware. It ships with 32 GB of DDR5 RAM, a 1 TB NVMe SSD, and AMD’s Ryzen 7 7840HS processor (8 cores, 16 threads, up to 5.1 GHz, RDNA 3 integrated graphics). At around $449, it offers a combination of specs that would have cost twice as much just two years ago.

The 7840HS is particularly well-regarded for mini PC Ollama setups. It has the same Radeon 780M integrated GPU as the UM773, but paired with 32 GB of dual-channel DDR5 that gives you access to the full range of practically useful models: mistral:7b, llama3.1:13b, codellama:13b, and even mixtral:8x7b (if you’re willing to wait a bit longer on generation). I’ve seen 30-45 tokens per second on 7B models and 15-22 tokens per second on 13B models on this chip, which makes for a genuinely pleasant interactive experience.

The 1 TB NVMe SSD is more practical than you might think. Ollama models are large. llama3.1:8b is about 4.7 GB, codellama:13b is 7.4 GB, mistral is 4.1 GB. Once you start hoarding models (and you will), a 500 GB drive fills up fast. Having a full terabyte from the start is genuinely useful.

Thermals under sustained inference load stay well-controlled. The SER7 runs warm but doesn’t throttle significantly during extended sessions. It’s not fanless, but the fan is quiet at normal operation levels.

Verdict: The Beelink SER7 is my top pick for most Ollama users. The combination of 32 GB RAM, fast CPU, and reasonable price hits the sweet spot. Buy this if you want to run serious local AI without spending Mac Mini money.

5. ASUS PN64 Mini PC: Best for Power Users and Future-Proofing

Best for: Running 34B models and demanding multi-model setups

The ASUS PN64 is the build-quality leader of this list. ASUS’s ProArt-inspired mini PC line is known for solid construction, excellent thermals, and a high degree of configurability. The PN64 ships with the Intel Core i7-13700H (14 cores, 20 threads, up to 5.0 GHz), 32 GB DDR5, and a 1 TB NVMe SSD.

The i7-13700H has 6 performance cores and 8 efficiency cores, which turns out to be a good architecture for Ollama. The P-cores handle the compute-heavy inference work while the E-cores manage background tasks, keeping the system responsive even during sustained generation. You’ll see around 25-40 tokens per second on 7B models and 12-20 tokens per second on 13B models.

What makes the PN64 stand out for serious users is expandability. It supports up to 64 GB of DDR5 RAM across two SO-DIMM slots, has dual M.2 slots for storage, and even has a slot for an optional eGPU or NIC depending on the config. If your Ollama ambitions grow to include 34B or 70B quantized models, upgrading to 64 GB DDR5 is a realistic and relatively affordable path.

ASUS’s thermal engineering on the PN64 is noticeably better than cheaper mini PCs. Under extended inference workloads, it maintains clock speeds without the throttling you sometimes see on other brands. The build quality is also a step up: it feels like it’ll last years, not months.

Verdict: The ASUS PN64 is for users who want longevity and expandability. If you’re planning to eventually run 34B+ models and want a machine that’ll grow with you, the PN64’s upgrade path and build quality make it worth the premium over Beelink options.

Full Feature Comparison

Which One Should You Buy?

Here’s my honest breakdown based on where you’re at:

• Just getting started and on a budget: Get the Beelink Mini S12 Pro. It’ll run 7B models, costs under $200, and barely uses any electricity. Perfect for learning Ollama without a major investment.
• Want solid performance without overspending: The Beelink SER7 is the pick. Thirty-two gigabytes of RAM and a fast Ryzen 7840HS means you can run 13B models smoothly today, and upgrade RAM later for larger models tomorrow.
• In the Apple ecosystem or prioritizing efficiency: The Mac Mini M4 is remarkable for what it draws in power. The unified memory architecture gives Ollama real advantages, and macOS support is first-class.
• Want the upgrade path and build quality: The ASUS PN64 is the long-term investment. ASUS’s build quality and the dual SO-DIMM slots give you a machine that’ll scale as models keep getting better.
• Mid-range with AMD’s iGPU advantage: The MINISFORUM UM773 Lite is the best option if you want to start at 16 GB and upgrade RAM yourself for significant savings.

Frequently Asked Questions

How much RAM do I actually need to run Ollama on a mini PC?

It depends entirely on which models you want to run. A 7B model (like llama3.2:7b or mistral:7b) needs about 5-6 GB of RAM, so 16 GB gives you comfortable headroom. A 13B model needs roughly 10-12 GB, so 16 GB is tight and 32 GB is more comfortable. For 34B models, you need at least 24 GB, and ideally 32 GB or more. If you want to run 70B models, even quantized, you’re looking at 40+ GB. My recommendation: get at least 16 GB to start, but 32 GB is the sweet spot for a versatile Ollama setup.

Can I use the integrated GPU for Ollama inference?

Yes, on AMD and Apple silicon machines, Ollama can offload some model layers to the integrated GPU for faster inference. On AMD Radeon 780M (found in the UM773 and SER7), Ollama uses ROCm or Vulkan compute for GPU layer offloading. On Apple M4, Metal GPU acceleration works seamlessly. On Intel machines, GPU offloading through Ollama is less mature, so most inference runs on the CPU. Even without GPU offloading, a fast CPU gives solid performance for 7B-13B models.

Is a mini PC powerful enough to use Ollama as a daily driver?

Absolutely, with the right model and machine. I use the Beelink SER7 daily for local coding assistance (codellama:13b), summarization, and general chat. Token generation speeds of 15-30 tokens per second on 13B models are comparable to many cloud API endpoints and feel snappy for interactive use. The key is matching model size to hardware: don’t try to run a 34B model on a 16 GB machine and expect a good experience.

Do I need a dedicated GPU to run Ollama locally?

No. Ollama runs great on CPU-only setups, and all of the mini PCs on this list will give you a usable experience for appropriately sized models. Dedicated GPUs (like NVIDIA cards) dramatically speed up inference and allow you to run larger models, but they add significant cost, power draw, and physical size. For a compact, quiet, low-power home AI setup, mini PCs with integrated graphics are a completely reasonable choice. You can always add a GPU later via a Thunderbolt eGPU enclosure if you outgrow your setup.

Can I run Ollama as a server on a mini PC for my whole home network?

Yes, and this is one of my favorite use cases. Ollama’s server mode lets you expose it to your local network, meaning you can run it on a mini PC in a closet and access it from your laptop, phone, or other devices via Open WebUI or any compatible client. All the mini PCs here except the Mac Mini can run headless Linux setups with Ollama as a systemd service. The Beelink Mini S12 Pro is especially compelling for this use case given its low power draw: it costs almost nothing to leave on 24/7.

Affiliate disclosure: This post contains affiliate links. If you buy through them, I may earn a small commission at no extra cost to you.

About The Author

Marcus Li

Marcus Li is a web designer and marketing specialist based in Vancouver, BC. After years of helping clients build their online presence, he discovered how AI tools could level the playing field for everyday entrepreneurs. He shares what actually works, no fluff, no hype.

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