Products · · NAS

TerraMaster F2-422

Processor
Intel Celeron J3455 / N3450 (Apollo Lake)
Memory (RAM)
4 or 8 GB
Released
2020

Product page (opens in a new tab)Discontinued 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 J3455 / N3450 (Apollo Lake) 4.0 GB 2.0 GB 15 GB/s 23 of 622 —
Intel Celeron J3455 / N3450 (Apollo Lake) · upgraded to the maker's maximum 8.0 GB 5.2 GB 15 GB/s 27 of 622 —

Specifications 4.0 GB configuration

MakerTerraMaster
TypeNAS
ProcessorIntel Celeron J3455 / N3450 (Apollo Lake) NAS (4 GB)
Memory (RAM)4.0 GB
Usable for AI models2.0 GB
Memory bandwidth15 GB/s
FP16 compute0.0 TFLOPS
Runs models onCPU cores
Drive bays2x SATA
NAS systemTOS 4.x
Containers (Docker)Yes
Virtual machinesYes
Memory upgradeDDR3 (module type not published) · up to 8.0 GB per the maker · non-ECC
AvailabilityDiscontinued (sold used or refurbished)
NotesIntel Celeron quad-core 1.5 GHz (J3455 per sibling models; not named in the maker post). CPU only: 2016 Apollo Lake Celeron without AVX and 8 GB of DDR3 at most (as far as published). Only the smallest quantised models could load, and very slowly. Treat as not practical for chat models.
Product pagewww.terra-master.com (opens in a new tab)Discontinued

Running AI on the TerraMaster F2-422

It can, with limits: expect small models only, at modest speed. Models run on its CPU cores, and this configuration leaves about 2.0 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

Best LLMs for the TerraMaster F2-422 4.0 GB

Quality Model Quant Memory Speed Runs
47Fair quality (C) · 47 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. LFM3 1.7BLiquid AI Q3_K_M 1.7 GB 3.8 tok/s Slowtight fit
39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. PaddleOCR-VL 1.6 0.9BBaidu Q4_K_M 1.5 GB 5.3 tok/s Slow
39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. MiniCPM-OCR 1.0 1.3BOpenBMB Q4_K_M 1.7 GB 3.9 tok/s Slow
42Fair quality (C) · 42 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. LFM3-VL 2BLiquid AI Q3_K_M 1.8 GB 3.4 tok/s Slowtight fit
34Basic quality (D) · 34 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. MiniCPM5 1BOpenBMB Q4_K_M 1.6 GB 4.7 tok/s Slow
38Basic quality (D) · 38 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. MiniCPM-V 4.7 1BOpenBMB Q4_K_M 1.8 GB 3.3 tok/s Slowtight fit
30Basic quality (D) · 30 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. LFM2.5 1.2B InstructLiquid AI Q4_K_M 1.6 GB 4.3 tok/s Slow
33Basic quality (D) · 33 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. TMax 2BAi2 Q3_K_M 1.8 GB 3.5 tok/s Slowtight fit
30Basic quality (D) · 30 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. LFM2.5 2.6BLiquid AI Q2_K 2.0 GB 3.1 tok/s Slowtight fit
24Basic quality (D) · 24 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 0.8BAlibaba Q4_K_M 1.4 GB 5.8 tok/s Slow

Best Image models for the TerraMaster F2-422 4.0 GB

Quality Model Quant Memory Speed Runs
39Basic quality (D) · 39 of 100: how capable the model is at FP8, whatever it runs on. Match score 48/100 (quality, fit, speed and memory use) sets the ranking. SDXL TurboStability AI FP8 1.6 GB 2.0 min SlowCPU offload
42Fair quality (C) · 42 of 100: how capable the model is at FP16, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. Realistic Vision V5.1SG161222 FP16 1.3 GB 17.8 min Very slowCPU offload
40Fair quality (C) · 40 of 100: how capable the model is at FP16, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. DreamShaper 8Lykon FP16 1.3 GB 17.8 min Very slowCPU offload
28Basic quality (D) · 28 of 100: how capable the model is at FP16, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. SD TurboStability AI FP16 1.3 GB 71 s SlowCPU offload
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking. Stable Diffusion 1.5Stability AI / Runway FP16 1.3 GB 17.8 min Very slowCPU offload
30Basic quality (D) · 30 of 100: how capable the model is at FP16, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking. LCM DreamShaper v7Tsinghua (LCM) FP16 1.3 GB 3.7 min Very slowCPU offload

Best Video models for the TerraMaster F2-422 4.0 GB

None of the video models in our catalog fit in this much memory.

Best Decision models for the TerraMaster F2-422 4.0 GB

Quality Model Quant Memory Speed Runs
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 1.6 GB 51.4 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 1.6 GB 51.4 s Very slow
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. GLiNER2.5 DecideFastino Q8_0 1.2 GB 22.7 s Very slow
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. FRIDA-DecisionsSber AI Q8_0 1.7 GB 38.0 s Very slow
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Reranker 0.6BAlibaba Q8_0 1.6 GB 51.4 s Very slow
79Great quality (A) · 79 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. mxbai-rerank-large v2Mixedbread Q5_K_M 1.9 GB 131.8 s Very slowtight fit
76Great quality (A) · 76 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. Llama Prompt Guard 2 86MMeta Q8_0 1.0 GB 26.0 s Very slow
76Great quality (A) · 76 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. mxbai-rerank-base v2Mixedbread Q8_0 1.3 GB 42.0 s Very slow

Other products with the Intel Celeron J3455 / N3450 (Apollo Lake) NAS (4 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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