Choosing between an Intel N100 and Ryzen 5 5500U mini PC for Ollama takes more than comparing core counts. This guide connects CPU, memory, cooling, and system design to a repeatable way to test which models each machine can run comfortably.

The Processor Specifications

Intel lists the N100 as a 4-core, 4-thread processor with a maximum turbo frequency of 3.4 GHz, 6 MB of cache, a 6 W thermal design power, and one memory channel. Its supported memory types include DDR4-3200, DDR5-4800, and LPDDR5-4800. Intel’s product page lists 16 GB as the processor’s maximum memory size, subject to the system design.

AMD lists the Ryzen 5 5500U as a 6-core, 12-thread processor with a 2.1 GHz base clock, boost up to 4.0 GHz, 8 MB of L3 cache, a default 15 W TDP, and a configurable 10–25 W range. It supports DDR4-3200 or LPDDR4-4266 and includes Radeon graphics with seven graphics cores—not Vega 6.

Published processor specification Intel N100 Ryzen 5 5500U
CPU cores / threads 4 / 4 6 / 12
Maximum boost 3.4 GHz Up to 4.0 GHz
Memory channels 1 Check system implementation; processor supports DDR4/LPDDR4
Published memory types DDR4, DDR5, LPDDR5 DDR4, LPDDR4
Processor graphics Intel UHD, 24 execution units Radeon graphics, 7 graphics cores
Published TDP 6 W 15 W default, 10–25 W configurable

These are processor specifications, not whole-system measurements. They do not establish a mini PC’s wall power, fan noise, sustained temperature, RAM capacity, or inference speed.

The LPDDR5 and “Fanless” Traps

N100 systems are sold in different memory and cooling configurations. ASUS, for example, lists fan and fanless PN43 variants with an N100. Confirm all of the following for the exact stock-keeping unit:

  • RAM type, installed capacity, and whether it is soldered or socketed;
  • the manufacturer’s supported maximum RAM;
  • active fan or passive cooling;
  • number and size of storage slots;
  • Ethernet speed and controller;
  • included power supply; and
  • operating-system license, if one is advertised.

The same caution applies to Ryzen mini PCs. The number of slots and supported capacity belong to the computer design, not the CPU model.

What Determines CPU LLM Performance

Local generation repeatedly reads model weights, so memory bandwidth can be a major constraint. Core count and vector instruction support matter too. The Ryzen part has more cores and threads; the N100 has one memory channel. Those facts suggest different performance characteristics, but they do not justify invented token-per-second figures.

Real results depend on:

  • the exact model and quantization;
  • RAM speed, channel population, and firmware configuration;
  • Ollama and backend version;
  • context length and prompt size;
  • whether any layers use a supported GPU backend;
  • the machine’s sustained power and thermal limits; and
  • other work running on the box.

Integrated graphics also share system memory. The Ryzen 5 5500U does not have a fixed “2 GB video memory limit” comparable to a discrete GPU. Firmware may reserve some memory, and the driver/backend may use shared memory differently. Ollama’s hardware support changes over time; check its current GPU documentation and confirm the processor split with ollama ps rather than assuming either integrated GPU will help.

Choose the Workload Before the Computer

A 3B-class model used for short summaries is a different workload from an 8B model with long retrieved documents. Before buying, decide:

  1. which exact model tag you intend to run;
  2. the context length you need;
  3. how many simultaneous users or requests you expect;
  4. whether the service must stay responsive during generation; and
  5. whether the computer will also act as a NAS, media server, or desktop.

Model download size is not total runtime memory. Leave room for the operating system, Ollama, the key-value cache, and other services. Avoid swap-dependent configurations for interactive use; they may work, but storage paging can make latency unpredictable.

Install Ollama and Confirm the Execution Path

Use Ollama’s current installer for your operating system, then pull a model small enough for the installed RAM. The commands below use a 3B model as a starting point, not as a guarantee of quality for every task:

ollama pull llama3.2:3b
ollama run llama3.2:3b

In another terminal, inspect the loaded model:

ollama ps

The PROCESSOR column shows whether the model is on CPU, GPU, or split between them. Record this with the benchmark; otherwise a backend change can look like a CPU comparison.

Context length increases memory requirements. Set it deliberately rather than assuming the model’s advertised maximum is appropriate for a small computer. Ollama exposes context settings in the application and through request options; test the smallest context that meets the use case first.

Run a Repeatable Test on Both Machines

Ollama’s API returns timing counters, so you can collect results without estimating speed by eye. Warm the model once, then send the same non-streaming request on each machine:

curl -s http://localhost:11434/api/generate \
  -d '{
    "model": "llama3.2:3b",
    "prompt": "Explain the difference between backups and snapshots in 200 words.",
    "stream": false,
    "options": {"num_ctx": 2048, "seed": 42, "temperature": 0}
  }' > result.json

Then calculate the rates from nanosecond durations. Subtract cached prompt tokens because Ollama reports prompt evaluation time for uncached tokens; a zero duration gives a null rate:

jq '{
  load_seconds: (.load_duration / 1000000000),
  prompt_tokens_per_second: (if .prompt_eval_duration > 0 then
    ((.prompt_eval_count - (.prompt_eval_cached_count // 0)) / (.prompt_eval_duration / 1000000000)) else null end),
  output_tokens_per_second: (if .eval_duration > 0 then
    (.eval_count / (.eval_duration / 1000000000)) else null end)
}' result.json

Repeat at least five times, keep every result, and report the median plus the range. Test a cold load separately from warm requests. Save:

  • exact mini PC model and BIOS version;
  • RAM type, speed, capacity, and slot population;
  • operating system and Ollama version;
  • model tag and full digest from curl http://localhost:11434/api/tags;
  • context length and prompt;
  • ollama ps output;
  • ambient conditions if thermal behavior is important; and
  • wall-power or sound-meter method if publishing power or noise figures.

Without those records, exact throughput, temperature, power, and decibel tables are anecdotes, not repeatable benchmarks.

Buying Guidance for a Mini PC

Prices change too quickly for one model number to be a permanent recommendation. Compare the complete cost on the day of purchase: system, RAM, storage, power supply, tax, and any operating-system license.

An N100 system can make sense when a small, low-concurrency model and low platform cost are priorities. Prefer a clearly documented RAM configuration and cooling design. A Ryzen 5 5500U system offers more CPU cores and threads, which may help inference and general multitasking, but the result still depends on the particular machine’s memory and sustained power configuration.

If local LLM work is the primary purpose, RAM capacity and upgradeability deserve more weight than a vendor’s “AI PC” label. If the machine will also host ordinary services, reserve CPU and memory for them. The better purchase is the system whose documented configuration fits the workload—not whichever processor wins an unsupported tokens-per-second table.

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