CodeGemma 2B on Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (192 GB)

Can the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (192 GB) run CodeGemma 2B?

Yes, it runs great

Runs great. At Q4_K_M it needs about 16.1 GB of 173 GB usable, with an estimated speed of 36.5 tok/s.

Best quant: Q4_K_M
Generation speed: 36.5 tok/s
Prompt processing: 191 tok/s
First token (1K prompt): 5.4 s
Largest safe context: 8K

Best app to run it on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (192 GB)

On Linux

Ollama

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

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 codegemma:2b-code-q4_K_M

llama.cpp Usually the first to support new model architectures.
KoboldCpp

Want an agent? CodeGemma 2B supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 CodeGemma 2B

Recommended for the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (192 GB): Q4_K_M. 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 codegemma:2b-code-q4_K_M

Quantized files · GGUF

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

huggingface.co/google/codegemma-2b-GGUF (opens in a new tab)

ollama run hf.co/google/codegemma-2b-GGUF

Original weights · Hugging Face

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

huggingface.co/google/codegemma-2b (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).

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 13Basic quality (D) · 13 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q4_K_M, 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 Runs great: How well it runs on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (192 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.	6.0 / 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.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	14.3 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	2.2 / 15
Match score	47.5 / 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 Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (192 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	15.6 GB	173 GB	60.0 tok/s	Runs great	9Basic quality (D) · 9 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking.
`Q3_K_M`	15.8 GB	173 GB	47.2 tok/s	Runs great	11Basic quality (D) · 11 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.
`Q4_K_M`	16.1 GB	173 GB	36.5 tok/s	Runs great	13Basic quality (D) · 13 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.
`Q5_K_M`	16.4 GB	173 GB	30.2 tok/s	Runs great	13Basic quality (D) · 13 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking.
`Q6_K`	16.6 GB	173 GB	25.3 tok/s	Runs well	13Basic quality (D) · 13 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking.
`Q8_0`	17.2 GB	173 GB	18.6 tok/s	Runs well	14Basic quality (D) · 14 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.
`F16`	19.6 GB	173 GB	8.6 tok/s	Slow	14Basic quality (D) · 14 of 100: how capable the model is at F16, whatever it runs on. Match score 40/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab) 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

14Basic quality (D) · 14 of 100: how capable the model is at F16, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB F16 · 41.3 tok/s · Runs great
14Basic quality (D) · 14 of 100: how capable the model is at F16, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER F16 · 57.8 tok/s · Runs great
14Basic quality (D) · 14 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 Q8_0 · 67.7 tok/s · Runs great
14Basic quality (D) · 14 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT F16 · 63.2 tok/s · Runs great
14Basic quality (D) · 14 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER F16 · 77.1 tok/s · Runs great
14Basic quality (D) · 14 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER F16 · 84.4 tok/s · Runs great

Quick answers

How much memory does CodeGemma 2B need on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 192GB?

At Q4_K_M it needs about 16.1 GB. The Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 192GB has 173 GB usable.

How fast is CodeGemma 2B on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 192GB?

About 36.5 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of CodeGemma 2B should I use on the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS 192GB?

Q4_K_M. 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.