QNAP TVS-AIh1688ATX

Products · · NAS

QNAP TVS-AIh1688ATX

Processor
Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop)
Memory (RAM)
32, 48 or 192 GB
Released
2025

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 Core Ultra 5 225 / 7 265 / 9 285 (desktop) 32.0 GB 26.2 GB 90 GB/s 381 of 622 —
Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) 48.0 GB 40.3 GB 90 GB/s 387 of 622 —
Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) · upgraded to the maker's maximum 192 GB 173 GB 90 GB/s 417 of 622 —

Specifications 32.0 GB configuration

MakerQNAP
TypeNAS
ProcessorIntel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (32 GB)
Memory (RAM)32.0 GB
Usable for AI models26.2 GB
Memory bandwidth90 GB/s
FP16 compute0.8 TFLOPS
Runs models onCPU cores
Drive bays12 x 3.5-inch SATA 6Gb/s, 3Gb/s + 4 x 2.5-inch U.2 NVMe Gen 4 x2 slots
NAS systemQuTS hero
Containers (Docker)Yes
Virtual machinesYes
Memory upgradeDDR5 UDIMM · 4 slots · up to 192 GB per the maker · ECC
Graphics card optionsPCIe: 3 ; Slot 1: PCIe Gen4 x16 ; Slot 2: PCIe Gen4 x4 ; Slot 3: PCIe Gen4 x4. External GPU: 2 x USB4 ports; optional Thunderbolt 5 via QXP-T52P card. eGPU use not documented by QNAP. Maker-supported graphics cards: QNAP FAQ: graphics card in PCIe slot 1 (Gen4 x16). NVIDIA driver packages cover GTX 10 to RTX 50 / RTX PRO; Intel Arc B70 support added in QuTS hero h6.1.
NotesIntel Core Ultra 9 24-core (8P+16E)/24-thread, up to 5.6 GHz or Intel Core Ultra 7 20-core (8P+12E)/20-thread, up to…. Core Ultra 7/9 with NPU and iGPU, 32-48 GB ECC DDR5 (max 192 GB), and a PCIe Gen4 x16 slot for a full discrete GPU. Runs Ollama in Container Station on CPU; with an NVIDIA (or, from h6.1, Intel Arc B70) card it runs QNAP's on-prem Qsirch LLM and Qsirch AI Mode.
Product pagewww.qnap.com (opens in a new tab)

Running AI on the QNAP TVS-AIh1688ATX

Yes, it runs AI models on the NAS itself. Models run on its CPU cores, and this configuration leaves about 26.2 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 + Open WebUI In a container (Docker)
Ollama / Open WebUI or any other LLM server image In a container (Docker)
Any Linux VM running an LLM runtime In a virtual machine
Ollama In a container (Docker)
Open WebUI In a container (Docker)

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

AI features from QNAP

FeatureWhat it doesWhere the AI runs
Qsirch RAG Search (cloud LLM mode) Natural-language search and Q&A over NAS files; retrieval runs on the NAS but the LLM is a cloud API (Gemini, OpenAI, Azure OpenAI or OpenAI-compatible). Cloud service or another computer
QNAP AI Core (QuMagie, QVR Face, QVR Human, Qsirch OCR) On-device image-recognition engine (faces, objects, similar photos, people counting, OCR) accelerated by the NPU / Intel CPU+GPU+NPU. Vision models only; the product page names no on-device LLM for it. On the NAS
QuMagie + QNAP AI Core Photo management with on-device face, object and scene recognition (QNAP AI Core image-recognition engine). On the NAS
Qsirch AI Semantic Search Natural-language/semantic file and image search computed locally; QNAP requires a 64-bit x86 NAS with at least 8 GB RAM. On the NAS
Qsirch on-premises RAG / LLM Core Runs open models (DeepSeek R1, Gemma 3, gpt-oss 20B, Mistral 7B, Phi-4) locally for RAG search. Requires an NVIDIA GPU assigned to Container Station; QNAP states CPU-only is not supported. Needs QuTS hero h5.2.1+ and Qsirch 6.0.0+. On the NAS
QuAgent (beta) + AI Service Center (beta) Conversational NAS assistant on QTS 6.1 / QuTS hero h6.1; uses either a cloud model or an on-premises model (compatible GPU recommended). QNAP lists x86 NAS "x74 series and above", TVS-AI and QAI series as supported. On the NAS

Best LLMs for the QNAP TVS-AIh1688ATX 32.0 GB

Quality Model Quant Memory Speed Runs
88Excellent quality (A+) · 88 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.6 35B-A3BAlibaba Q4_K_M 24.8 GB 27.7 tok/s Runs welltight fit
88Excellent quality (A+) · 88 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. MiniCPM-V 4.7 35B-A3BOpenBMB Q4_K_M 24.3 GB 27.7 tok/s Runs welltight fit
88Excellent quality (A+) · 88 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 35B-A3BAlibaba Q4_K_M 24.8 GB 27.7 tok/s Runs welltight fit
87Excellent quality (A+) · 87 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. Nex N2 Mini (35B-A3B)Nex-AGI Q4_K_M 24.3 GB 27.7 tok/s Runs welltight fit
87Excellent quality (A+) · 87 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 26B-A4BGoogle Q4_K_M 18.8 GB 22.3 tok/s Runs well
86Excellent quality (A+) · 86 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. KAT-Coder V2.5 Dev (35B-A3B)Kuaishou Q4_K_M 24.0 GB 27.7 tok/s Runs welltight fit
86Excellent quality (A+) · 86 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. Sage-A1.5 35B-A3BInternScience Q4_K_M 24.8 GB 27.7 tok/s Runs welltight fit
83Great quality (A) · 83 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. GLM-4.7 Flash (30B-A3B)Zhipu AI Q4_K_M 22.2 GB 27.7 tok/s Runs well
85Excellent quality (A+) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.5 35B-A3BDeepReinforce Q4_K_M 24.8 GB 27.7 tok/s Runs welltight fit
84Great quality (A) · 84 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Apodex 1.1 Mini (35B-A3B)Apodex Q4_K_M 24.8 GB 27.7 tok/s Runs welltight fit

Best Image models for the QNAP TVS-AIh1688ATX 32.0 GB

Quality Model Quant Memory Speed Runs
90Excellent quality (A+) · 90 of 100: how capable the model is at FP8, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. Krea 2 TurboKrea FP8 20.2 GB 3.8 min Very slow
82Great quality (A) · 82 of 100: how capable the model is at FP8, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 9BBlack Forest Labs FP8 19.9 GB 1.6 min Slow
78Great quality (A) · 78 of 100: how capable the model is at FP16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4BBlack Forest Labs FP16 18.1 GB 58 s Slow
87Excellent quality (A+) · 87 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. ERNIE-ImageBaidu Q8 GGUF 18.9 GB 24.6 min Very slow
85Excellent quality (A+) · 85 of 100: how capable the model is at NF4, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Ideogram 4Ideogram NF4 21.8 GB 26.7 min Very slow
90Excellent quality (A+) · 90 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. FLUX 3 Image KleinBlack Forest Labs Q8 GGUF 12.8 GB 6.4 min Very slow

Best Video models for the QNAP TVS-AIh1688ATX 32.0 GB

Quality Model Quant Memory Speed Runs
83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. Bernini-R A14B (video generation + editing)ByteDance FP8 20.3 GB 11.0 h Very slowencoder unloaded after use
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. HY-OmniWeaving (8.3B, unified video)Tencent FP8 24.6 GB 7.7 h Very slowtight fit
70Great quality (A) · 70 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.2 S2V 14B (speech to video)Alibaba Q4 GGUF 21.6 GB 19.4 h Very slow
68Good quality (B) · 68 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 I2V 14B 480PAlibaba Q4 GGUF 20.6 GB 17.5 h Very slow
79Great quality (A) · 79 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. LTX-2.3Lightricks Q4 GGUF 24.5 GB 8.2 h Very slowtight fit · encoder unloaded after use

Best Decision models for the QNAP TVS-AIh1688ATX 32.0 GB

Quality Model Quant Memory Speed Runs
78Great quality (A) · 78 of 100: how capable the model is at F16, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. OpenAI Privacy FilterOpenAI F16 3.8 GB 37 ms Runs great
68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. Llama Prompt Guard 2 22MMeta F16 0.8 GB 99 ms Runs great
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. GLiNER2.5 DecideFastino Q8_0 1.2 GB 288 ms Runs well
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3.5Jina AI Q8_0 3.6 GB 657 ms Slow
66Good quality (B) · 66 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. GLiClass Base v3Knowledgator Q8_0 0.8 GB 114 ms Runs great
84Great quality (A) · 84 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. Jina Reranker v3Jina AI Q8_0 3.6 GB 657 ms Slow
64Good quality (B) · 64 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. DeBERTa v3 Base Zero-shot v2.0Moritz Laurer Q8_0 0.8 GB 108 ms Runs great
74Great quality (A) · 74 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. GLiClass ModernBERT Large v3Knowledgator Q8_0 1.1 GB 236 ms Runs well

Other products with the Intel Core Ultra 5 225 / 7 265 / 9 285 (desktop) NAS (32 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.

Was this page helpful?