IceWhale ZimaCube

Products · · NAS

IceWhale ZimaCube

Processor
Intel N100 / N95
Memory (RAM)
8, 16 or 32 GB
Launch price
$649
Released
2024

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 N100 / N95 8.0 GB 5.2 GB 38 GB/s 147 of 622 $649
Intel N100 / N95 16.0 GB 12.0 GB 38 GB/s 169 of 622 —
Intel N100 / N95 · community-tested upgrade, above the maker's stated maximum 32.0 GB 26.2 GB 38 GB/s 200 of 622 —

Specifications 16.0 GB configuration

MakerIceWhale
TypeNAS
ProcessorIntel N100 / N95 NAS (16 GB)
Memory (RAM)16.0 GB
Usable for AI models12.0 GB
Memory bandwidth38 GB/s
FP16 compute0.2 TFLOPS
Runs models onCPU cores
Drive bays6x 3.5/2.5in SATA + 4x M.2 NVMe (7th bay, 800 MB/s)
NAS systemZimaOS; other OSes can be installed
Containers (Docker)Yes
Virtual machinesYes
Memory upgradeDDR4 3200 SO-DIMM · 1 slot · up to 16.0 GB per the maker · 32.0 GB reported working by owners · non-ECC
Graphics card optionsPCIe: 1x PCIe 3.0 slot (maker lists it as 'Gen 3.0, 2 GB/s x1'; lane count not stated). Half-height cards up to…. External GPU: Zima's 800W OCuLink GPU Docking Station ($179.90, PCIe 4.0 x4, GPUs up to 650 W) is listed as compatible with…. Maker-supported graphics cards: NVIDIA Tesla P4 (8 GB, 50 W); NVIDIA GTX 1650 LP (4 GB, 75 W); NVIDIA RTX 3050 LP (6 GB, 70 W).
NotesIntel Processor N100. N100 with 8 GB shipped: CPU-only small models. The maker recommends i5-1235U or better for the Intel build of ZimaOS AI.
Product pagewww.zimaspace.com (opens in a new tab)

Running AI on the IceWhale ZimaCube

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 From the NAS app store
Open WebUI From the NAS app store
AnythingLLM From the NAS app store

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

AI features from IceWhale

FeatureWhat it doesWhere the AI runs
ZimaOS AI (AI search in Files) Optional module installed with 'zpkg install zimaos_ai' (NVIDIA) or 'zpkg install zimaos_ai_intel' (Intel iGPU); downloads a local model and adds natural-language/semantic search of files. On the NAS

Best LLMs for the IceWhale ZimaCube 16.0 GB

Quality Model Quant Memory Speed Runs
76Great quality (A) · 76 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. K2 Horizon 7BInstitute of Foundation Models Q4_K_M 8.3 GB 3.5 tok/s Slow
66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. Mellum2.1 Thinking (12B-A2.5B)JetBrains Q4_K_M 9.1 GB 12.1 tok/s Runs well
74Great quality (A) · 74 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. SenseNova Loop 9B PreviewSenseTime Q4_K_M 7.9 GB 3.3 tok/s Slow
58Good quality (B) · 58 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. Maple Preview (20B-A1B)DeepGrove Q3_K_M 11.6 GB 25.1 tok/s Runs welltight fit
74Great quality (A) · 74 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. MiMo V2.6 Distill Qwen 9BXiaomi Q4_K_M 7.6 GB 3.3 tok/s Slow
73Great quality (A) · 73 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. Ornith 1.5 9BDeepReinforce Q4_K_M 7.7 GB 3.3 tok/s Slow
72Great quality (A) · 72 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 9BAlibaba Q4_K_M 7.7 GB 3.3 tok/s Slow
71Great quality (A) · 71 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.0 9BDeepReinforce Q4_K_M 7.7 GB 3.3 tok/s Slow
69Good quality (B) · 69 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 12BGoogle Q3_K_M 8.1 GB 3.4 tok/s Slow
70Great quality (A) · 70 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. TMax 9BAi2 Q4_K_M 7.3 GB 3.5 tok/s Slow

Best Image models for the IceWhale ZimaCube 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 79/100 (quality, fit, speed and memory use) sets the ranking. FLUX 3 Image KleinBlack Forest Labs Q4 GGUF 10.2 GB 23.3 min Very slow
76Great quality (A) · 76 of 100: how capable the model is at FP8, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4BBlack Forest Labs FP8 10.2 GB 3.1 min Very slow
78Great quality (A) · 78 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. Z-Image TurboAlibaba (Tongyi) Q4 GGUF 10.1 GB 10.2 min Very slow
79Great quality (A) · 79 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. ERNIE-Image TurboBaidu Q4 GGUF 11.3 GB 13.1 min Very slowtight fit
76Great quality (A) · 76 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. Z-ImageAlibaba (Tongyi) Q4 GGUF 10.1 GB 69.6 min Very slow
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 1.5 h Very slowtight fit · encoder unloaded after use

Best Video models for the IceWhale ZimaCube 16.0 GB

Quality Model Quant Memory Speed Runs
49Fair quality (C) · 49 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. LTX-Video 2BLightricks FP8 10.8 GB 76.7 min Very slowtight fit
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 47.9 h Very slowtight fit · CPU offload
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. HY-OmniWeaving (8.3B, unified video)Tencent FP8 7.8 GB 35.2 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 14.3 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 15.5 h Very slowencoder unloaded after use

Best Decision models for the IceWhale ZimaCube 16.0 GB

Quality Model Quant Memory Speed Runs
78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. OpenAI Privacy FilterOpenAI Q8_0 2.4 GB 197 ms Runs well
90Excellent quality (A+) · 90 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Reranker 4BAlibaba Q8_0 6.1 GB 28.8 s Very slow
86Excellent quality (A+) · 86 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. Jina Reranker v3.5Jina AI Q8_0 2.4 GB 4.3 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 58.6 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 28.8 s Very slow
84Great quality (A) · 84 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. Jina Reranker v3Jina AI Q8_0 2.4 GB 4.3 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 115.9 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 27.6 s Very slow

Other products with the Intel N100 / N95 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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