Products · IceWhale · NAS
IceWhale ZimaBoard 2 832
- Processor
- Intel N150
- Memory (RAM)
- 8 GB
- Launch price
- $339
- Released
- 2025
Specifications
| Maker | IceWhale |
|---|---|
| Type | NAS |
| Processor | Intel N150 NAS (8 GB) |
| Memory (RAM) | 8.0 GB |
| Usable for AI models | 5.2 GB |
| Memory bandwidth | 38 GB/s |
| FP16 compute | 0.2 TFLOPS |
| Runs models on | CPU cores |
| Drive bays | 2x SATA 6 Gb/s with power (cable-attached drives; no drive enclosure) |
| NAS system | ZimaOS preinstalled; other OSes can be installed |
| Containers (Docker) | Yes |
| Virtual machines | Yes |
| Memory upgrade | LPDDR5 4800 (soldered; maker spec table says LPDDR5, some copy says… · up to 8.0 GB per the maker · non-ECC |
| Graphics card options | PCIe: 1x PCIe 3.0 slot (open-ended x4 connector; maker table gives the generation only). External GPU: Zima's 800W OCuLink GPU Docking Station ($179.90, PCIe 4.0 x4, GPUs up to 650 W) connects through a…. |
| Notes | Intel Processor N150. N150 with 8 GB soldered single-channel RAM: CPU-only small models. An external GPU on the PCIe x4 slot (maker sells a powered dock) changes that. |
| Product page | shop.zimaspace.com (opens in a new tab) |
Running AI on the IceWhale ZimaBoard 2 832
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
| App | How it installs |
|---|---|
| Ollama | From the NAS app store |
| Open WebUI | From the NAS app store |
| AnythingLLM | From the NAS app store |
| Ollama-Nvidia | From the NAS app store |
| LocalAI | From the NAS app store |
AI features from IceWhale
| Feature | What it does | Where 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 |
| ZimaOS-AI (AI search / Assist in Files) | Optional ZimaOS module (zpkg install zimaos_ai for NVIDIA, zimaos_ai_intel for Intel iGPU) that extracts features from images, audio and video with a locally downloaded model and gives keyword and natural-language search. | On the NAS |
Best LLMs for the IceWhale ZimaBoard 2 832
| Quality | Model | Quant | Memory | Speed | Runs |
|---|---|---|---|---|---|
| 58Good quality (B) · 58 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. | Gemma 4 E2B | Q4_K_M | 4.2 GB | 13.7 tok/s | Runs well |
| 50Fair quality (C) · 50 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. | Ling 3.0 Tiny (8B-A1.3B) | Q3_K_M | 5.1 GB | 28.4 tok/s | Runs welltight fit |
| 41Fair quality (C) · 41 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. | Unlimited OCR 2 (3B-A0.6B) | Q4_K_M | 3.6 GB | 44.5 tok/s | Runs great |
| 57Good quality (B) · 57 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. | Nemotron Labs Dory 1.0 4B | Q3_K_M | 3.4 GB | 10.2 tok/s | Runs well |
| 38Basic quality (D) · 38 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. | Unlimited OCR (3B-A0.6B) | Q4_K_M | 3.6 GB | 44.5 tok/s | Runs great |
| 57Good quality (B) · 57 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. | MiniCPM5 2B | Q4_K_M | 2.9 GB | 12.5 tok/s | Runs well |
| 49Fair quality (C) · 49 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. | Nemotron 3.5 Bolt E4B | Q2_K | 4.4 GB | 11.5 tok/s | Runs well |
| 36Basic quality (D) · 36 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. | Granite 4.0 H Tiny (7B-A1B) | Q3_K_M | 4.5 GB | 35.9 tok/s | Runs welltight fit |
| 47Fair quality (C) · 47 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. | Nanbeige4.2 3B | Q2_K | 4.2 GB | 12.4 tok/s | Runs well |
| 47Fair quality (C) · 47 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. | Gemma 4 E4B | Q2_K | 4.4 GB | 11.5 tok/s | Runs well |
Best Image models for the IceWhale ZimaBoard 2 832
| 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 Klein | Q8 GGUF | 4.3 GB | 29.1 min | Very slowCPU offload |
| 72Great quality (A) · 72 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. | FLUX.2 [klein] 4B | Q4 GGUF | 4.6 GB | 4.3 min | Very slowtight fit · encoder unloaded after use |
| 39Basic quality (D) · 39 of 100: how capable the model is at FP8, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. | SDXL Turbo | FP8 | 4.2 GB | 28 s | Runs well |
| 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 Turbo | FP8 | 4.4 GB | 10.0 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-Image | Q4 GGUF | 4.1 GB | 2.0 h | Very slowCPU offload |
| 81Great quality (A) · 81 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. | MOSS-Image SFT | Q8 GGUF | 4.1 GB | 71.4 min | Very slowCPU offload |
Best Video models for the IceWhale ZimaBoard 2 832
| 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 56/100 (quality, fit, speed and memory use) sets the ranking. | AnimateDiff v3 (SD 1.5) | FP16 | 4.7 GB | 68.2 min | Very slowtight fit |
| 49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. | LTX-Video 2B | FP8 | 4.0 GB | 1.7 h | Very slowCPU offload |
| 44Fair quality (C) · 44 of 100: how capable the model is at FP8, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. | CogVideoX 2B | FP8 | 4.7 GB | 4.0 h | Very slowtight fit · CPU offload |
Best Decision models for the IceWhale ZimaBoard 2 832
| 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 Filter | Q8_0 | 2.4 GB | 197 ms | Runs well |
| 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 v3.5 | Q8_0 | 1.9 GB | 4.3 s | Very slow |
| 87Excellent quality (A+) · 87 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. | Qwen3 Reranker 4B | Q5_K_M | 4.2 GB | 28.7 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 v3 | Q8_0 | 1.9 GB | 4.3 s | Very slow |
| 80Great quality (A) · 80 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. | GLiNER2.5 Decide | Q8_0 | 1.2 GB | 1.9 s | Very slow |
| 85Excellent quality (A+) · 85 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. | zerank-2 | Q5_K_M | 4.2 GB | 28.7 s | Very slow |
| 85Excellent quality (A+) · 85 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. | Shieldstral 1.0 3B | Q6_K | 4.4 GB | 27.6 s | Very slow |
| 80Great quality (A) · 80 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. | FRIDA-Decisions | Q8_0 | 1.7 GB | 3.2 s | Very slow |
Other products with the Intel N150 NAS (8 GB)
UC250Product page (opens in a new tab)
CyberData Vault CF500Product page (opens in a new tab)
AOOSTAR R1 N150 2-Bay NAS Mini PCProduct page (opens in a new tab)Discontinued
X86-P6 pocket NASProduct page (opens in a new tab)
N150-K / N355 Mini-ITX NAS motherboard (6x SATA, 2x i226-V)Product page (opens in a new tab)- N18 NAS Mini-ITX motherboardProduct page (opens in a new tab)
N8 6-bay NAS motherboard (Intel N100 / N150; sold elsewhere as MW-N100-NAS class board)Product page (opens in a new tab)
Y104A (4x SATA + 4x NVMe hybrid NAS)Product page (opens in a new tab)
P5-4M 6-bay Pocket NAS (N100 / N150 / i3-N305 / Core 3 N355)Product page (opens in a new tab)
N22 8-bay 10GbE NAS motherboard (N150 / i3-N300)Product page (opens in a new tab)
N29 dual-10GbE NAS motherboard (N150, 6x SATA + 3x NVMe)Product page (opens in a new tab)
CyberData CF500Product page (opens in a new tab)
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.