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Can the Intel Core i5-8400T / i7-8700T (8th gen desktop) NAS (4 GB) run Gemma 2 2B?

19Basic quality (D) · 19 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 38/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly (far over memory limit)

Slow (far over memory limit). At Q2_K it needs about 2.3 GB of 2.0 GB usable, with an estimated speed of 21.4 tok/s.

Best quant
Q2_K
Generation speed
21.4 tok/s
Prompt processing
42.1 tok/s
First token (1K prompt)
24.3 s
Largest safe context
3K

Best app to run it on the Intel Core i5-8400T / i7-8700T (8th gen desktop) NAS (4 GB)

On Linux

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • One command with Ollama’s own build of this model.
  • Serves an OpenAI-compatible API that other apps and coding tools can use.
ollama run gemma2:2b-instruct-q2_K
  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.
  • KoboldCpp Splits the model between GPU memory and system RAM, which this setup needs.

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download Gemma 2 2B

Recommended for the Intel Core i5-8400T / i7-8700T (8th gen desktop) NAS (4 GB): Q2_K. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run gemma2:2b-instruct-q2_K

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/lmstudio-community/gemma-2-2b-it-GGUF (opens in a new tab)

ollama run hf.co/lmstudio-community/gemma-2-2b-it-GGUF

Original weights · Hugging Face

Full-precision files from Google, for fine-tuning, vLLM, Transformers.

huggingface.co/google/gemma-2-2b-it (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Google approves it (usually within minutes to a few hours).

Official repo · GitHub

Reference code, inference scripts and docs from Google. The weights themselves download from Hugging Face.

github.com/google-deepmind/gemma (opens in a new tab)

Google DeepMind’s official Gemma library.

git clone https://github.com/google-deepmind/gemma

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 19Basic quality (D) · 19 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 38/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Slowfar over memory limit
How well it runs on the Intel Core i5-8400T / i7-8700T (8th gen desktop) NAS (4 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.8.8 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.4.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.11.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score38.8 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the Intel Core i5-8400T / i7-8700T (8th gen desktop) NAS (4 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 2.3 GB 2.0 GB 21.4 tok/s Slowfar over memory limit 19Basic quality (D) · 19 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 38/100 (quality, fit, speed and memory use) sets the ranking.
Q3_K_M 2.5 GB 2.0 GB — Won't run 23Basic quality (D) · 23 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. GGUF (Q3_K_L) (opens in a new tab)
Q4_K_M 2.9 GB 2.0 GB — Won't run 26Basic quality (D) · 26 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 3.1 GB 2.0 GB — Won't run 27Basic quality (D) · 27 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 3.4 GB 2.0 GB — Won't run 27Basic quality (D) · 27 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 4.0 GB 2.0 GB — Won't run 28Basic quality (D) · 28 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 6.5 GB 2.0 GB — Won't run 28Basic quality (D) · 28 of 100: how capable the model is at F16, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does Gemma 2 2B need on the Intel Core i5-8400T / i7-8700T (8th gen desktop) NAS 4GB?

At Q2_K it needs about 2.3 GB. The Intel Core i5-8400T / i7-8700T (8th gen desktop) NAS 4GB has 2.0 GB usable.

How fast is Gemma 2 2B on the Intel Core i5-8400T / i7-8700T (8th gen desktop) NAS 4GB?

About 21.4 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Gemma 2 2B should I use on the Intel Core i5-8400T / i7-8700T (8th gen desktop) NAS 4GB?

Q2_K. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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