Running Open-Source LLMs on a $300 Mini PC: Intel N100 vs. Ryzen 5 with Ollama

For $300, you can run a capable 7–8 billion parameter model on your desk—but the hardware you pick determines whether that feels like a conversation or a staring contest. An Intel N100 box and a used Ryzen 5 5500U system both get the job done through Ollama. The experience diverges the moment you ask anything longer than a sentence.

The Contenders

The Intel N100 is a 4‑core, 4‑thread processor built on Gracemont efficiency cores. It runs at an 800 MHz base with a burst to 3.4 GHz, draws 6 W, and lives inside a huge number of fanless or barely‑audible mini PCs. Memory bandwidth is the bottleneck: the N100 supports only single‑channel DDR4 or DDR5, topping out around 25 GB/s.

The Ryzen 5 5500U is a Zen 2 laptop chip with 6 cores, 12 threads, and a configurable TDP between 10 W and 25 W. It brings dual‑channel DDR4 (most mini PCs ship with two SODIMM slots) and considerably more L3 cache. The integrated Vega graphics don’t accelerate LLM inference directly, but the extra threads and bandwidth matter more than any spec sheet suggests.

N100 machines almost always ship with soldered LPDDR5. That keeps power low but locks you into whatever capacity you bought. Ryzen‑based mini PCs use socketed SODIMMs, so bumping to 32 GB or 64 GB later is straightforward—and that matters for larger models or longer context windows.

Ollama Setup and Model Choices

Getting Ollama running is identical on both platforms: grab the Linux installer or the Windows preview, pull a model, and start chatting. The real decisions happen in model selection and quantization.

For a 16 GB machine, the sweet spot is a 4‑bit quantized 7B or 8B model. Llama 3 8B Q4_K_M fits in under 6 GB and leaves headroom for a 4096‑token context. Gemma 2 9B at Q4 is larger but still fits. Anything in the 13B–14B range requires aggressive 3‑bit quantization and will spill into swap if you aren’t careful—on either machine.

Both platforms default to pure CPU inference. The N100 has no dedicated GPU worth using, and the Ryzen’s integrated Vega 6 can technically run inference through ROCm or DirectML, but driver headaches and the 2 GB video memory limit make it worse than letting the CPU cores handle the work.

A few adjustments help on both machines:

• Set num_ctx explicitly. Start at 2048 tokens to keep memory usage predictable, then push to 4096 or 8192 once you see how headroom holds.
• Disable Ollama’s keep‑alive or shorten it to 10 minutes. With 16 GB, every megabyte counts, and an idle loaded model is wasted memory.
• Use the smallest workable quantization. Q4_K_M is a good default; Q5_0 improves output quality slightly at roughly 20 % more memory. For testing larger models, Q3_K_L is the floor.

On the N100, single‑channel bandwidth makes model loading sluggish—often 30–45 seconds for a 5 GB file. The Ryzen 5 loads the same file in roughly half that time.

Performance: Tokens per Second

Token generation speed gets the headlines, but prompt processing time matters just as much for any conversation longer than a few exchanges. These numbers reflect Ollama’s default settings on Linux with a warm model.

Llama 3 8B – Q4_K_M (4.9 GB)

Gemma 2 9B – Q4_K_M (5.4 GB)

The N100 generates 7–9 tokens per second on Llama 3, which reads about like a person typing—maybe a hair slower. Prompts longer than 500 tokens introduce visible pauses. The Ryzen 5, with nearly double the memory bandwidth and more threads, delivers responses that appear almost instantly for short prompts and sustain a comfortable reading pace even with a 2000‑token conversation history.

Where the N100 really struggles is prompt evaluation. Paste a long document or a chain‑of‑thought example, and it can take 15–20 seconds just to process the input before the first token appears. The Ryzen 5 cuts that lag enough that you’re not reaching for your phone while you wait.

Phi‑3 mini, a 3.8B model, runs at a snappy 18–25 tokens per second even on the N100—if your use case fits a small model, the difference narrows considerably. Mistral 7B at Q4 behaves almost identically to Llama 3 on both machines. For a deeper look at how 7B models compare on constrained hardware, see the RTX 4070 GPU Showdown: Llama 3.1 8B vs Mistral 7B vs Phi-3 Medium.

Both machines hit a hard wall at 13B models. On the N100, a Q4_K_M Llama 2 13B (7.4 GB) generates at 2–3 tokens per second and frequently stutters during prompt processing. The Ryzen 5 manages 5–7 tokens per second with the same model—usable but not pleasant—and benefits enormously from 32 GB of RAM, which keeps the model entirely out of swap.

Thermals, Power, and Noise

The N100 practically doesn’t make sound. Most mini PCs built around it are completely fanless; the chip rarely exceeds 70 °C under sustained load, and the chassis barely gets warm. Idle power draw of 5–7 W means a $15 USB‑C power supply works fine. You can leave it running 24/7 with zero noise and a negligible electricity bill.

Ryzen 5 mini PCs ship with small blower fans, and they do spin up during inference. A 15‑minute conversation with Gemma 2 9B keeps the fan at an audible 35–40 dBA. The chip settles around 75–80 °C under continuous load. It’s not annoying—more like a laptop fan—but it’s not silent. Idle power jumps to 9–12 W, and a full month of occasional use might add a dollar or two to your power bill. Not much, but not zero.

If the box lives in a living room or on a desk where you spend quiet hours, the N100’s silence is a genuine advantage. In a closet or basement office, the Ryzen’s fan noise is a non‑issue.

What You’ll Actually Notice Day to Day

Beyond the numbers, here’s how the two machines feel to live with.

System responsiveness during inference: The N100 has four threads total, and Ollama occupies all of them while generating. The machine remains usable for light tasks—a browser, a terminal—but opening a new app or switching workspaces introduces lag. The Ryzen 5’s 12 threads mean you can keep a dozen tabs open, compile code, or stream music, and the LLM chugs along in the background without the system feeling bogged down.

Model experimentation: The Ryzen 5’s socketed RAM makes it possible to try a 34B Yi model at Q3_K_L after a trip to the memory store. The N100 is stuck at 16 GB, and that hard cap eliminates entire categories of fine‑tunes and reasoning models. You’ll spend less time fiddling with quantization and context limits on the Ryzen.

Setup friction: Both platforms run Ollama identically on Linux. Windows gives the Ryzen 5 slightly better driver maturity for GPU offload experiments, but if you’re running a mini PC as an LLM server, you’ll likely put Ubuntu or Debian on it anyway. The N100 can run headless with zero maintenance for months; the Ryzen 5 needs a dusting now and then.

Expandability: N100 mini PCs rarely offer more than a single M.2 slot and a couple of USB ports. Some Ryzen 5 boxes include dual M.2 slots, 2.5 GbE, and a swappable Wi‑Fi card. That matters if you plan to repurpose the machine later as a NAS or media server. If you go that route, make sure you aren’t saddling your storage with the wrong drives—the SMR drive trap can quietly ruin ZFS performance.

Which One to Buy

The N100 box wins on simplicity and silence. If you want a dedicated, always‑on Ollama endpoint that handles 7B–8B models without noise, without management, and without ever thinking about it, it’s the right choice. A Beelink S12 Pro or equivalent costs around $160 with 16 GB of RAM, keeping you well under budget. Accept the slower prompt processing, treat it like a patient research assistant, and you’ll be happy.

The Ryzen 5 5500U mini PC is the pick for anyone who wants to push beyond small models. At $250–300 for a barebone unit plus DDR4, you get roughly double the tokens per second, the ability to upgrade to 32 GB or more, and a machine that stays usable while inferring. It’s louder, hotter, and slightly more expensive to run, but the flexibility is hard to overstate. If you plan to experiment with larger models, custom fine‑tunes, or long‑context tasks, the extra money buys a dramatically better experience.

One final consideration: if you can stretch the budget by another $50–$80, a Ryzen 7 5800H or 5825U mini PC with 16 GB pushes generation speed to the 25–35 tok/s range—fast enough that you stop thinking about the hardware entirely. But strictly inside the $300 boundary, the choice between silent patience and noisy capability is the one you’ll be making.