IceWhale ZimaBlade 7700

Products · · NAS

IceWhale ZimaBlade 7700

Processor
Intel Celeron J3455 / N3450 (Apollo Lake)
Memory (RAM)
8 or 16 GB
Launch price
$119
Released
2023

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) · ships without memory; with 8 GB fitted 8.0 GB 5.2 GB 15 GB/s 27 of 622 $119
Intel Celeron J3455 / N3450 (Apollo Lake) · ships without memory; with 16 GB fitted 16.0 GB 12.0 GB 15 GB/s 27 of 622 —

Specifications 8.0 GB configuration

MakerIceWhale
TypeNAS
ProcessorIntel Celeron J3455 / N3450 (Apollo Lake) NAS (8 GB) · ships without memory; with 8 GB fitted
Memory (RAM)8.0 GB
Usable for AI models5.2 GB
Memory bandwidth15 GB/s
FP16 compute0.0 TFLOPS
Runs models onCPU cores
Drive bays2x SATA 3.0 (external drives); 32GB eMMC
NAS systemCasaOS
Containers (Docker)Yes
Virtual machinesYes
Memory upgradeDDR3L SODIMM · 1 slot · up to 16.0 GB per the maker · non-ECC
Graphics card optionsPCIe: 1x PCIe 2.0 x4 (exposed). External GPU: No Thunderbolt. Maker sells an 800W OCuLink GPU Docking Station ($179.90) with a PCIe-to-OCuLink adapter….
AvailabilityDiscontinued (sold used or refurbished)
NotesIntel Celeron N3450 / J3455 / Atom E3950 (Apollo Lake, varies by batch). Apollo Lake quad-core (no AVX) with one DDR3L slot up to 16GB: only very small models at low speed; many prebuilt Ollama/llama.cpp binaries fall back to slow non-AVX code paths.
Product pagewww.zimaspace.com (opens in a new tab)Discontinued

Running AI on the IceWhale ZimaBlade 7700

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. 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
Auto GPT Server (use case on maker page) Maker lists an AutoGPT server as a use case; AutoGPT calls a remote model API by default. Cloud service or another computer

Best LLMs for the IceWhale ZimaBlade 7700 8.0 GB

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 IceWhale ZimaBlade 7700 8.0 GB

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 IceWhale ZimaBlade 7700 8.0 GB

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 IceWhale ZimaBlade 7700 8.0 GB

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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