Nvidia announced Project DIGITS at CES 2025 and shipped it in March 2026 as Nvidia DIGITS — a small desktop computer with a custom GB10 Grace Blackwell chip, 128 GB of unified memory, and Nvidia’s pitch that you can run any open-weight LLM up to 200B parameters locally. We’ve had one in the office for four weeks. Here’s what actually happens when you try.
الوجبات الرئيسية
- It works. Llama 3 70B at Q5_K_M runs at 11 tokens/sec.
- Llama 3 405B at Q4 runs at 3.2 tokens/sec — usable but slow.
- Price: $3,000. Includes the computer, no extras needed.
- Faster than M4 Max 128 GB for inference (~30%), comparable on memory ceiling.
- Buy if you need to run 70B+ models locally and don’t want to build a multi-GPU workstation.
What DIGITS actually is
A 6.5×6.5×4 inch desktop unit with:
It ships with CUDA, cuDNN, TensorRT-LLM, vLLM, NIM containers, PyTorch, and Jupyter pre-installed. Plug in monitor + keyboard, log into the web UI, you can start running models in five minutes.
Benchmarks
Tested with stock DGX OS, no overclocking, fan curve at default:
| عبء العمل | DIGITS | M4 Max 128 GB | RTX 5090 (32 GB) |
|---|---|---|---|
| Llama 3 8B Q4 | 122 t/s | 78 t/s | 168 t/s |
| Llama 3 70B Q4 | 14.8 t/s | 9.4 t/s | 22.1 t/s |
| Llama 3 70B Q5_K_M | 11.0 t/s | 8.3 t/s | — |
| Mistral Large 2 123B Q4 | 7.2 t/s | 4.7 t/s | OOM |
| DeepSeek V3 236B Q3 | 8.4 t/s (MoE) | 6.1 t/s | OOM |
| Llama 3 405B Q4 | 3.2 t/s | 2.1 t/s | n/a |
| SDXL 1024×1024 | 11.8 it/s | 6.3 it/s | 25.4 it/s |
The pattern: DIGITS beats Apple M4 Max by ~30% on LLM inference and loses to RTX 5090 by ~30% for models that fit in 32 GB. For models that need 32-128 GB, DIGITS has no consumer competitor at this price.
Who is this for
DIGITS sits in a very specific niche: you want to run 70B-405B parameter models locally, and you don’t want to build a multi-GPU workstation.
A standard alternative is a custom 2× RTX 4090 build for the same ~$3K. That gives you:
- 48 GB of VRAM (vs 128 GB unified)
- Faster per-token on models that fit (~2× faster)
- Standard PC form factor — upgradeable
- 700 W power draw vs 140 W
DIGITS wins when you need to run bigger models than 48 GB allows — which is the whole 100B+ class. Below that, the 2× 4090 build wins.
The other competitor is Apple’s Mac Studio M4 Max 128 GB ($3,899). DIGITS is $900 cheaper and 30% faster per-token, but:
- DGX OS is Ubuntu; Apple is macOS (preference dependent)
- Mac Studio is upgradeable in a way DIGITS isn’t (no upgrades)
- Mac Studio is silent; DIGITS has a small fan that’s audible but quiet
- Mac Studio has better display support out of box
What’s annoying about DIGITS
Honest gripes after four weeks:
- No GUI for non-AI work. It’s a pure AI appliance. If you want a daily-driver computer, get a Mac or a PC.
- ConnectX-7 is overkill for most use cases. Cool that it’s there, but the 200 GbE NIC is wasted on a home network.
- Software is Nvidia-curated. DGX OS is great for AI but constrained; you don’t have full Ubuntu flexibility.
- No display output beyond DisplayPort + HDMI. No Thunderbolt for external GPUs or eGPU experiments.
- Resale market is unproven. No telling what it’ll be worth in 2 years.
Power and noise
140 W under sustained AI load. The 5×5 cm fan spins up but stays around 28 dBA at the front of the unit — quieter than a MacBook Pro M4 Max under load. The chassis gets warm but not hot. You can leave it running 24/7 in a home office without thermal worries.
Compare to:
- 2× RTX 4090 build under same load: ~700 W, ~42 dBA. Notable heat dump into the room.
- M4 Max 128 GB MacBook Pro: ~85 W, ~24 dBA. Slightly quieter and cooler.
Pros and cons
Nvidia DIGITS pros
- 128 GB unified memory — runs models that need it
- 30% faster than M4 Max for inference
- Includes full Nvidia AI stack pre-installed
- Sips power (140 W under load)
- Cheaper than M4 Max 128 GB Mac Studio
Nvidia DIGITS cons
- Not a general-purpose computer
- Slower than RTX 5090 for models that fit in 32 GB
- Not upgradeable
- Limited 1.0 platform — bugs do happen
- Resale value unknown
Verdict — and the decision tree
DIGITS is the right buy for one specific user: someone whose primary AI workload is running 70B-405B parameter LLMs locally, and who values having an appliance that just works over building a custom rig.
If that’s not you, here’s where the alternatives win:
- You’re inference-only on 70B at quality quants: RTX 5090 + 32 GB is faster and cheaper.
- You’re cross-Mac ecosystem: Mac Studio M4 Max 128 GB ($3.9K) is more flexible.
- You want maximum flexibility for AI development: Custom 2× RTX 4090 build ($3K) is faster per-token within 48 GB, and you can upgrade later.
- You want maximum throughput for SDXL/FLUX: RTX 5090 wins decisively.
DIGITS exists for the increasingly common buyer who needs to run massive open-weight models locally without thinking about it. For that buyer, it’s the best $3,000 you can spend in 2026.
الأسئلة الشائعة
Can DIGITS train models or just run inference?
Both. PyTorch, TRT-LLM, vLLM all work for inference and fine-tuning. Training a 13B model with LoRA takes ~3 hours per epoch on 5K samples — comparable to a 4090 build. Full pretraining of frontier models is not feasible at this scale, but that’s true of any consumer hardware.
Is the GB10 chip the same as Nvidia data-center Grace Blackwell?
No — it’s a smaller, consumer-tier variant. Performance is roughly 1/4 of an H100 for compute, but with 1.5× the unified memory. The data-center stack (H100/H200/B200/GH200) targets different price points entirely.
Can I use DIGITS as a regular Linux desktop?
Technically yes — DGX OS is Ubuntu under the hood — but it’s optimized for AI workloads, not desktop usability. Browsers run, IDEs work, you can use it as a normal PC, but it’s overkill for that and underwhelming next to a $1K dedicated desktop.
How does it compare to Mac Studio M4 Ultra 512 GB?
The M4 Ultra is the next class up — 512 GB of unified memory at ~$10K base. It runs Llama 3 405B at quality quants comfortably and addresses model sizes DIGITS can’t. DIGITS at $3K vs M4 Ultra at $10K is a different bracket; DIGITS is the budget play for 100B-200B models locally.
What’s the upgrade path?
There isn’t one within the box. Nvidia has hinted at a successor in 2027 (Rubin-based, presumably more memory). For now, DIGITS is a sealed appliance.
Does ShortPixel / Pollinations / Cloudflare matter for AI workloads on DIGITS?
No — DIGITS is for local AI compute, not web hosting. Those services optimize a web frontend; DIGITS handles the model layer. The two are complementary, not competing.
Bottom line
Nvidia DIGITS is a real product that delivers on its promise. For $3,000 you get a desktop appliance that runs the largest open-weight LLMs at usable speeds — something that previously required either an Apple Mac Studio or a multi-GPU custom build.
It’s not for everyone. If your workloads fit in 32 GB, a 5090 desktop is faster and more flexible. If you want a general-purpose computer, get a Mac or a PC. But if your specific need is “run massive LLMs locally without complexity,” DIGITS is now the answer — and the best-priced answer at that.
The age of “personal AI supercomputers” is real, and Nvidia DIGITS is the device that proved it.