TerraMaster F2-425

Products · · NAS

TerraMaster F2-425

Processor
Intel Celeron N5105 / N5095
Memory (RAM)
4 or 16 GB
Launch price
$270
Released
2025

Product page (opens in a new tab) Full AI report

Configurations

Pick one to see which models it runs. More memory lets larger models fit; bandwidth sets how fast they answer.

Configuration Memory Usable for AI Bandwidth LLMs that run well Launch price
Intel Celeron N5105 / N5095 4.0 GB 2.0 GB 24 GB/s 52 of 622 $270
Intel Celeron N5105 / N5095 · upgraded to the maker's maximum 16.0 GB 12.0 GB 24 GB/s 72 of 622 —

Specifications 16.0 GB configuration

MakerTerraMaster
TypeNAS
ProcessorIntel Celeron N5105 / N5095 NAS (16 GB) · upgraded to the maker's maximum
Memory (RAM)16.0 GB
Usable for AI models12.0 GB
Memory bandwidth24 GB/s
FP16 compute0.1 TFLOPS
Runs models onCPU cores
Drive bays2x SATA
NAS systemTOS 6
Containers (Docker)Yes
Virtual machinesYes
Memory upgradeDDR4 SODIMM (non-ECC) · 1 slot · up to 16.0 GB per the maker · non-ECC
NotesIntel Celeron N5095. CPU only: 4-core low-power chip, no NPU and no GPU slot. Ollama in Docker is the maker's suggested route; expect small models (1-4B, quantised) at slow speeds. No tokens-per-second benchmark was found for this model. Ships with 4 GB, so a memory upgrade is needed first. Official maximum is 16 GB.
Product pagewww.terra-master.com (opens in a new tab)

Running AI on the TerraMaster F2-425

It can, with limits: expect small models only, at modest speed. Models run on its CPU cores, and this configuration leaves about 12.0 GB for them after the NAS system and its storage services. The grades below assume the NAS isn’t busy with backups or media work at the same time. More memory is the upgrade that matters most: see the configurations.

Apps you can install to run models

AppHow it installs
Ollama In a container (Docker)
Open WebUI In a container (Docker)

Apps that run LLMs on a NAS, with their container images

AI features from TerraMaster

FeatureWhat it doesWhere the AI runs
Terra Photos (TerraMaster Photos) Photo library with face and scene recognition; the maker says the AI processing uses a local model on the NAS. This is an image classifier, not a chat model. On the NAS

Best LLMs for the TerraMaster F2-425 16.0 GB

Quality Model Quant Memory Speed Runs
79Great quality (A) · 79 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. K2 Horizon 7BInstitute of Foundation Models Q6_K 10.1 GB 0.9 tok/s Very slow
78Great quality (A) · 78 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 12BGoogle Q4_K_M 9.5 GB 1.0 tok/s Very slow
77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. MiMo V2.6 Distill Qwen 9BXiaomi Q6_K 9.5 GB 0.9 tok/s Very slow
77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. SenseNova Loop 9B PreviewSenseTime Q6_K 10.0 GB 0.9 tok/s Very slow
76Great quality (A) · 76 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.5 9BDeepReinforce Q6_K 9.7 GB 0.9 tok/s Very slow
75Great quality (A) · 75 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 9BAlibaba Q6_K 9.7 GB 0.9 tok/s Very slow
74Great quality (A) · 74 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.0 9BDeepReinforce Q6_K 9.7 GB 0.9 tok/s Very slow
73Great quality (A) · 73 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. TMax 9BAi2 Q6_K 9.2 GB 0.9 tok/s Very slow
66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. Mellum2.1 Thinking (12B-A2.5B)JetBrains Q4_K_M 9.1 GB 4.7 tok/s Slow
69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. UI-Mate 9BTencent Q6_K 9.5 GB 0.9 tok/s Very slow

Best Image models for the TerraMaster F2-425 16.0 GB

Quality Model Quant Memory Speed Runs
84Great quality (A) · 84 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. FLUX 3 Image KleinBlack Forest Labs Q4 GGUF 10.2 GB 46.4 min Very slow
76Great quality (A) · 76 of 100: how capable the model is at FP8, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4BBlack Forest Labs FP8 10.2 GB 6.1 min Very slow
78Great quality (A) · 78 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. Z-Image TurboAlibaba (Tongyi) Q4 GGUF 10.1 GB 20.3 min Very slow
76Great quality (A) · 76 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. Z-ImageAlibaba (Tongyi) Q4 GGUF 10.1 GB 2.3 h Very slow
79Great quality (A) · 79 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. ERNIE-Image TurboBaidu Q4 GGUF 11.3 GB 26.0 min Very slowtight fit
87Excellent quality (A+) · 87 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. ERNIE-ImageBaidu Q8 GGUF 11.2 GB 3.0 h Very slowtight fit · encoder unloaded after use

Best Video models for the TerraMaster F2-425 16.0 GB

Quality Model Quant Memory Speed Runs
83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. Bernini-R A14B (video generation + editing)ByteDance FP8 10.7 GB 95.2 h Very slowtight fit · CPU offload
49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2BLightricks FP8 10.8 GB 2.5 h Very slowtight fit
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. HY-OmniWeaving (8.3B, unified video)Tencent FP8 7.8 GB 70.1 h Very slowCPU offload
55Good quality (B) · 55 of 100: how capable the model is at FP16, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 T2V 1.3BAlibaba FP16 8.4 GB 28.5 h Very slowencoder unloaded after use
55Good quality (B) · 55 of 100: how capable the model is at FP16, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 VACE 1.3BAlibaba FP16 10.1 GB 30.8 h Very slowencoder unloaded after use

Best Decision models for the TerraMaster F2-425 16.0 GB

Quality Model Quant Memory Speed Runs
90Excellent quality (A+) · 90 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Reranker 4BAlibaba Q8_0 6.1 GB 115.0 s Very slow
89Excellent quality (A+) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Reranker 8BAlibaba Q6_K 8.6 GB 233.9 s Very slow
88Excellent quality (A+) · 88 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. zerank-2ZeroEntropy Q8_0 6.1 GB 115.0 s Very slow
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3.5Jina AI Q8_0 2.4 GB 17.2 s Very slow
90Excellent quality (A+) · 90 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Skywork Reward V2 Llama 3.1 8BSkywork Q8_0 10.4 GB 463.8 s Very slowtight fit
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Shieldstral 1.0 3BMistral AI Q8_0 5.9 GB 110.1 s Very slow
84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3Jina AI Q8_0 2.4 GB 17.2 s Very slow
78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. OpenAI Privacy FilterOpenAI Q8_0 2.4 GB 777 ms Slow

Other products with the Intel Celeron N5105 / N5095 NAS (16 GB)

Spec sources

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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