NVIDIA DGX Spark
{"review": "Оснащений суперчіпом NVIDIA GB10 Grace Blackwell, цей міні-ПК забезпечує продуктивність петафлопс-рівня для локального навчання ШІ та інференсу, маючи 128 ГБ уніфікованої пам'яті LPDDR5x та 4 ТБ NVMe-накопичувач. Вирізняється винятковою для свого класу компактністю (1.2 кг) та потужним набором інтерфейсів, включно з 10G Ethernet та Wi-Fi 7, що спрощує інтеграцію в мережеву інфраструктуру. Найкраще підходить для розробників великих мовних моделей та дата-саєнтистів, яким потрібна потужна локальна машина для прототипування та розгортання ШІ-застосунків."}
Огляд
The 30-Second Version
A petaflop AI brain in a mini PC body. It's useless for games but a revelation for running massive LLMs locally, just make sure you know Linux and don't overpay.
Pros & Cons
Переваги
- 128GB unified memory lets you run massive models locally 99th
- 4TB of fast NVMe storage out of the box 87th
- Dual 200G QSFP112 ports for serious networking throughput
- Whisper-quiet and tiny enough to disappear on a desk
Недоліки
- Useless for gaming or traditional GPU work
- DGX OS means you better be comfortable in a Linux terminal
- Reliability scores are worryingly low at the 12th percentile
- Price swings wildly from $4,679 to an absurd $589,990 depending on the seller
Думка власників
The Word on the Street
Як змінювалася думка власників із часом
ЕксклюзивНа основі того, коли покупці справді писали відгуки, - щоб побачити, чи виправдалися перші похвали.
На основі 12 датованих відгуків покупців, згрупованих за календарними кварталами. Аналіз за періодами - англійською.
Факти
Performance
What surprised us most is how this tiny box handles massive LLMs that would bring a $5,000 gaming rig to its knees. With 128GB of LPDDR5x, you can load 70B parameter models entirely in memory and get usable token generation speeds without touching the cloud. The 4TB NVMe drive is fast enough that even larger models swap in and out without making you wait forever. But don't get it twisted, the raw GPU compute sits in the 59th percentile. For traditional rendering or gaming, this thing is a dud. It's a scalpel for AI inference and fine-tuning, not a sledgehammer for everything else.
Specifications
Full Specifications
Processor
| CPU | Apple M2 |
| Cores | 20 |
| Frequency | 3.3 GHz |
Graphics
| GPU | NVIDIA RTX A1000 |
| Type | Discrete |
| VRAM | 8 GB |
| VRAM Type | GDDR6 |
Memory & Storage
| RAM | 128 GB |
| RAM Generation | DDR5 |
| Storage | 1000 GB |
| Storage Type | SSD |
Build
| Form Factor | mini |
| PSU | 240 |
| Weight | 3.1 kg / 6.9 lbs |
Connectivity
| USB-C Ports | 4 |
| USB Ports | 4 |
| HDMI | 1x HDMI |
| DisplayPort | 1x DisplayPort |
| Wi-Fi | Wi-Fi 7 |
| Bluetooth | Bluetooth 5.4 |
| Ethernet | 1x Ethernet |
System
| OS | Android 10 |
vs Competition
There's nothing quite like the DGX Spark in the consumer space, which makes comparisons tough. A Lenovo Legion 34IAS10 or HP Omen GT22 will run circles around it in games and creative apps, but they can't touch its AI memory capacity. The real competitor is a cloud instance. Renting a comparable GPU node with 128GB of VRAM will cost you thousands per month. If you're training or fine-tuning models constantly, the Spark pays for itself in under a year. If you just want to play with Stable Diffusion, a gaming desktop with an RTX 4090 is a much better and cheaper choice.
| Spec | NVIDIA DGX Spark | HP OMEN GT22-3080 | Lenovo Legion 90Y6003JUS | ASUS Republic of Gamers GM700TZ-BS978 | Apple Mac Studio M4 Max | MSI MEG Vision X AI VisXAI2NVZ9045 |
|---|---|---|---|---|---|---|
| CPU | Apple M2 | Intel Core Ultra 7 265K | Intel Core Ultra 9 | AMD Ryzen 9 9950X | Apple M4 Max | Intel Core Ultra 9 |
| RAM (GB) | 128 | 32 | 64 | 64 | 36 | 64 |
| Storage (GB) | 1000 | 2048 | 3072 | 2048 | 512 | 2048 |
| GPU | NVIDIA RTX A1000 | NVIDIA GeForce RTX 5080 | NVIDIA GeForce RTX 5080 | AMD Radeon RX 9070 XT | Apple M4 Max 32-core | NVIDIA GeForce RTX 5090 |
| Form Factor | mini | mid-tower | mid-tower | desktop | sff | mid-tower |
| Psu W | 240 | 850 | 1200 | 850 | - | 1300 |
| OS | Android 10 | Windows 11 Pro | Windows 11 Pro | Windows 11 Home | macOS | Windows 11 Pro |
| Compare | Compare | Compare | Compare | Compare |
| Товар | CPU | GPU | RAM | Роз'єми | Накопичувач | Надійність | Відгуки користувачів |
|---|---|---|---|---|---|---|---|
| NVIDIA DGX Spark | 38 | 60.8 | 98.7 | 87.1 | 51 | 11.6 | 6.9 |
| HP OMEN GT22-3080 Compare | 96 | 88.9 | 78.7 | 93.2 | 91.5 | 70.5 | 87.3 |
| Lenovo Legion 90Y6003JUS Compare | 97.5 | 88.9 | 96.4 | 91.7 | 96.4 | 70.5 | 84.2 |
| ASUS Republic of Gamers GM700TZ-BS978 Compare | 98.8 | 79.5 | 93.9 | 97.2 | 91.5 | 37.8 | 74.2 |
| Apple Mac Studio M4 Max Compare | 85.5 | 66.4 | 69.2 | 94.5 | 31 | 99.4 | 99.9 |
| MSI MEG Vision X AI VisXAI2NVZ9045 Compare | 97.5 | 90.7 | 97.4 | 98.2 | 91.5 | 37.8 | 87 |
Ціна
Value & Pricing
Value here is a moving target. If you snag this near the $4,679 mark from a reputable seller, it's a steal for an AI researcher who needs a local sandbox. At the other end of the spectrum, nearly six hundred grand is a joke. Check Best Buy first, they seem to have the most grounded pricing. For what this does, the low end of that spread makes it cheaper than a single high-end GPU server with comparable memory bandwidth.
Amazon.it 1 пропозиція Від 6 990 EUR
Price History
Докладніше
Overview
The NVIDIA DGX Spark is a weird, wonderful beast. It's a petaflop-scale AI supercomputer that sits on your desk and sips power from a 240W brick, and that's genuinely as cool as it sounds. The one thing to know is this isn't a PC. If you're expecting to install Windows, play Cyberpunk, or even have a normal desktop experience, walk away now. This is a dedicated Linux AI appliance packing 128GB of unified memory and a custom Grace Blackwell Superchip, and for the right person, it's an absolute dream.
Common Questions
Q: Can I play games on the DGX Spark?
No, and don't try. The GPU scores in the 59th percentile for a reason. It's built for AI compute, not DirectX. Grab a gaming desktop if you want to play.
Q: Do I need to know Linux to use this?
Absolutely. DGX OS is a custom Linux distribution. If the terminal scares you, this machine will be a very expensive brick. There's no hand-holding GUI here.
Q: What size AI models can this actually run?
With 128GB of unified memory, you can comfortably run 70B parameter models like Llama 3 fully in RAM. You can even stretch to larger models by offloading layers, but expect slower token generation.
Who Should Skip This
If you're looking for a general-purpose desktop, this isn't it. Go get a Dell Tower Plus or an HP Omen instead. If you just want to generate AI images as a hobby, a gaming PC with a 16GB GPU will serve you better for half the price. The Spark is for developers and researchers who need to train or fine-tune locally, not for casual users.
Verdict
Buy the DGX Spark if you know exactly which models you want to run locally and you're tired of cloud bills. It's a specialized instrument for AI developers and data science students who live in a terminal. For everyone else, this is an expensive paperweight. The low reliability score gives us pause, so maybe wait for a hardware revision if you're not in a rush. But if you need a personal AI server right now and find it under five grand, pull the trigger.