Products · · NAS

TerraMaster F8-422

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

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

Specifications

MakerTerraMaster
TypeNAS
ProcessorIntel Celeron J3455 / N3450 (Apollo Lake) NAS (8 GB)
Memory (RAM)8.0 GB
Usable for AI models5.2 GB
Memory bandwidth15 GB/s
FP16 compute0.0 TFLOPS
Runs models onCPU cores
Drive bays8x SATA
NAS systemTOS 4.x
Containers (Docker)Yes
Virtual machinesYes
Memory upgradeDDR3 (module type not published) · non-ECC
AvailabilityDiscontinued (sold used or refurbished)
NotesIntel Celeron J3455. 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 F8-422

It can, with limits: expect small models only, at modest speed. Models run on its CPU cores, and this configuration leaves about 5.2 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.

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 F8-422

Quality Model Quant Memory Speed Runs
65Good quality (B) · 65 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. Nemotron Labs Dory 1.0 4BNVIDIA Q5_K_M 4.3 GB 1.1 tok/s Very slow
64Good quality (B) · 64 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. Spark-X2.6 4BSparkLLM Q5_K_M 4.2 GB 1.0 tok/s Very slow
64Good quality (B) · 64 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. Nanbeige4.2 3BBOSS Zhipin (Nanbeige) Q4_K_M 5.0 GB 1.2 tok/s Very slowtight fit
59Good quality (B) · 59 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. TMax 4BAi2 Q5_K_M 4.3 GB 1.0 tok/s Very slow
58Good quality (B) · 58 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 4BAlibaba Q4_K_M 4.1 GB 1.1 tok/s Very slow
58Good quality (B) · 58 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. Nemotron 3.5 Bolt E4BNVIDIA Q3_K_M 5.1 GB 1.5 tok/s Very slowtight fit
57Good quality (B) · 57 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. Agents-A1 4BInternScience Q4_K_M 4.1 GB 1.1 tok/s Very slow
57Good quality (B) · 57 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 E4BGoogle Q3_K_M 5.1 GB 1.5 tok/s Very slowtight fit
55Good quality (B) · 55 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. Fara 1.5 4BMicrosoft Q4_K_M 4.1 GB 1.1 tok/s Very slow
57Good quality (B) · 57 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. Mage-VL-Cowork 4.7BMicrosoft Q4_K_M 5.1 GB 1.1 tok/s Very slowtight fit

Best Image models for the TerraMaster F8-422

Quality Model Quant Memory Speed Runs
90Excellent quality (A+) · 90 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. FLUX 3 Image KleinBlack Forest Labs Q8 GGUF 4.3 GB 1.6 h Very slowCPU offload
72Great quality (A) · 72 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4BBlack Forest Labs Q4 GGUF 4.6 GB 14.4 min Very slowtight fit · encoder unloaded after use
82Great quality (A) · 82 of 100: how capable the model is at FP8, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. Z-Image TurboAlibaba (Tongyi) FP8 4.4 GB 33.4 min Very slowCPU offload
81Great quality (A) · 81 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. ERNIE-ImageBaidu Q4 GGUF 4.1 GB 6.5 h Very slowCPU offload
81Great quality (A) · 81 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. MOSS-Image SFTOpenMOSS Q8 GGUF 4.1 GB 4.0 h Very slowCPU offload
80Great quality (A) · 80 of 100: how capable the model is at FP8, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. Z-ImageAlibaba (Tongyi) FP8 4.4 GB 3.8 h Very slowCPU offload

Best Video models for the TerraMaster F8-422

Quality Model Quant Memory Speed Runs
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. AnimateDiff v3 (SD 1.5)Shanghai AI Lab FP16 4.7 GB 3.8 h Very slowtight fit
49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2BLightricks FP8 4.0 GB 5.6 h Very slowCPU offload
44Fair quality (C) · 44 of 100: how capable the model is at FP8, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 2BZhipu AI FP8 4.7 GB 13.3 h Very slowtight fit · CPU offload

Best Decision models for the TerraMaster F8-422

Quality Model Quant Memory Speed Runs
87Excellent quality (A+) · 87 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Reranker 4BAlibaba Q5_K_M 4.2 GB 344.0 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 1.9 GB 51.4 s Very slow
85Excellent quality (A+) · 85 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. zerank-2ZeroEntropy Q5_K_M 4.2 GB 344.0 s Very slow
85Excellent quality (A+) · 85 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. Shieldstral 1.0 3BMistral AI Q6_K 4.4 GB 329.6 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 1.9 GB 51.4 s Very slow
82Great quality (A) · 82 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. mxbai-rerank-large v2Mixedbread Q8_0 2.7 GB 132.0 s Very slow
82Great quality (A) · 82 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. d1 3BLiquid AI Q8_0 4.4 GB 134.3 s Very slow
81Great quality (A) · 81 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. Intern-Decision 4BShanghai AI Lab Q5_K_M 4.3 GB 194.8 s Very slow

Other products with the Intel Celeron J3455 / N3450 (Apollo Lake) NAS (8 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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