Gemma 3 1B on Marvell OCTEON TX2 CN9130 NAS (32 GB)

Can the Marvell OCTEON TX2 CN9130 NAS (32 GB) run Gemma 3 1B?

Yes, it runs well

Runs well. At Q3_K_M it needs about 3.2 GB of 26.2 GB usable, with an estimated speed of 10.6 tok/s.

Best quant: Q3_K_M
Generation speed: 10.6 tok/s
Prompt processing: 12.5 tok/s
First token (1K prompt): 81.9 s
Largest safe context: 32K

Best app to run it on the Marvell OCTEON TX2 CN9130 NAS (32 GB)

On Linux on ARM boards

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 gemma3:1b

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

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 3 1B

Recommended for the Marvell OCTEON TX2 CN9130 NAS (32 GB): Q3_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 gemma3:1b

Quantized files · GGUF

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

Download the Q3_K_M file (opens in a new tab)

huggingface.co/unsloth/gemma-3-1b-it-GGUF (opens in a new tab)

ollama run hf.co/unsloth/gemma-3-1b-it-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/google/gemma-3-1b-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 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 16Basic quality (D) · 16 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q3_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 well: How well it runs on the Marvell OCTEON TX2 CN9130 NAS (32 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.	7.6 / 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.	7.7 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	2.8 / 15
Match score	43.1 / 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 Marvell OCTEON TX2 CN9130 NAS (32 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	3.1 GB	26.2 GB	13.4 tok/s	Runs well	14Basic quality (D) · 14 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 42/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	3.2 GB	26.2 GB	10.6 tok/s	Runs well	16Basic quality (D) · 16 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	3.4 GB	26.2 GB	8.2 tok/s	Slow	19Basic quality (D) · 19 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	3.5 GB	26.2 GB	6.8 tok/s	Slow	19Basic quality (D) · 19 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 42/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	3.6 GB	26.2 GB	5.7 tok/s	Slow	19Basic quality (D) · 19 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 42/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	3.8 GB	26.2 GB	4.2 tok/s	Slow	20Basic quality (D) · 20 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 42/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	4.7 GB	26.2 GB	1.9 tok/s	Very slow	20Basic quality (D) · 20 of 100: how capable the model is at F16, whatever it runs on. Match score 41/100 (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

20Basic quality (D) · 20 of 100: how capable the model is at F16, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 F16 · 78.3 tok/s · Runs great
20Basic quality (D) · 20 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. Intel Arc B580 F16 · 100 tok/s · Runs great
20Basic quality (D) · 20 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 3060 12GB F16 · 104 tok/s · Runs great
20Basic quality (D) · 20 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 SUPER F16 · 145 tok/s · Runs great
20Basic quality (D) · 20 of 100: how capable the model is at F16, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (24 GB) F16 · 67.7 tok/s · Runs great
20Basic quality (D) · 20 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (16 GB) Q8_0 · 64.6 tok/s · Runs great

Quick answers

How much memory does Gemma 3 1B need on the Marvell OCTEON TX2 CN9130 NAS 32GB?

At Q3_K_M it needs about 3.2 GB. The Marvell OCTEON TX2 CN9130 NAS 32GB has 26.2 GB usable.

How fast is Gemma 3 1B on the Marvell OCTEON TX2 CN9130 NAS 32GB?

About 10.6 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Gemma 3 1B should I use on the Marvell OCTEON TX2 CN9130 NAS 32GB?

Q3_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.