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Can the Marvell OCTEON TX2 CN9130 NAS (4 GB) run LFM2.5 350M?

9Basic quality (D) · 9 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q2_K it needs about 1.0 GB of 2.0 GB usable, with an estimated speed of 38.4 tok/s.

Best quant
Q2_K
Generation speed
38.4 tok/s
Prompt processing
35.7 tok/s
First token (1K prompt)
28.7 s
Largest safe context
87K

Best app to run it on the Marvell OCTEON TX2 CN9130 NAS (4 GB)

On Linux on ARM boards

llama.cpp

Command line + local API · runs on llama.cpp

  • Usually the first to support new model architectures.
llama-server -hf LiquidAI/LFM2.5-350M-GGUF:Q2_K
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

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 LFM2.5 350M

Quantized files · GGUF

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

huggingface.co/LiquidAI/LFM2.5-350M-GGUF (opens in a new tab)

ollama run hf.co/LiquidAI/LFM2.5-350M-GGUF

Original weights · Hugging Face

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

huggingface.co/LiquidAI/LFM2.5-350M (opens in a new tab)

No sign-in needed.

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

How it’s rated

Quality 9Basic quality (D) · 9 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 55/100 (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 Runs great
How well it runs on the Marvell OCTEON TX2 CN9130 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.4.4 / 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.14.7 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.11.5 / 15
Match score55.7 / 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 (4 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 1.0 GB 2.0 GB 38.4 tok/s Runs great 9Basic quality (D) · 9 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 1.0 GB 2.0 GB 30.2 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 55/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q4_K_M 1.1 GB 2.0 GB 23.4 tok/s Runs well 13Basic quality (D) · 13 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 1.1 GB 2.0 GB 19.3 tok/s Runs well 13Basic quality (D) · 13 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 1.1 GB 2.0 GB 16.2 tok/s Runs well 13Basic quality (D) · 13 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 1.2 GB 2.0 GB 11.9 tok/s Runs well 14Basic quality (D) · 14 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 1.6 GB 2.0 GB 5.5 tok/s Slow 14Basic quality (D) · 14 of 100: how capable the model is at F16, whatever it runs on. Match score 51/100 (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

Quick answers

How much memory does LFM2.5 350M need on the Marvell OCTEON TX2 CN9130 NAS 4GB?

At Q2_K it needs about 1.0 GB. The Marvell OCTEON TX2 CN9130 NAS 4GB has 2.0 GB usable.

How fast is LFM2.5 350M on the Marvell OCTEON TX2 CN9130 NAS 4GB?

About 38.4 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of LFM2.5 350M should I use on the Marvell OCTEON TX2 CN9130 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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