1TB vs 2TB vs 4TB NVIDIA GB10: Are You Paying for Storage You Don't Need?

For most buyers, 2 TB is the sweet spot, and on the ASUS Ascent GX10 it is barely more expensive than 1 TB: S$8,300 against S$8,100 at SourceIT (listed 28 Oct 2026). Storage does not change what a GB10 can run or how fast; every GB10 system has the same chip and the same 128 GB of memory. It decides how many models, datasets and containers you can keep on the box. 1 TB suits one person running a few models. 4 TB suits fine-tuning, large datasets or a shared server with a library of models. On some GB10 models the bigger tier also brings a faster drive, and on most you can replace the SSD later.

What storage do GB10 systems offer?

Drive details from NVIDIA documentation, Dell's specification sheet and reviews by ServeTheHome, Notebookcheck and StorageReview. No GB10 system has a full-length M.2 2280 slot. Prices listed 28 Oct 2026.

How big are AI models on disk?

From Hugging Face model repositories and llama.cpp's DGX Spark benchmark file.

Models are rarely stored once. The same model often exists in two or three formats, because different tools want different files: a GGUF for llama.cpp and Ollama, safetensors for vLLM, and an optimised build for TensorRT-LLM. Hugging Face also keeps its own download cache. Add container images for serving tools, which can run to tens of gigabytes each, plus the operating system, and a 1 TB drive fills faster than the model list suggests.

What fills each tier?

• 1 TB: the OS and NVIDIA's software, a few container images, and roughly five to ten models of 10 to 70 GB. Fine for one developer who prunes regularly.
• 2 TB: a working library of models in several formats, a RAG document index, some datasets and room to experiment without deleting things weekly. The sensible default.
• 4 TB: fine-tuning datasets and checkpoints, multiple versions of large models for evaluation, transcribed audio archives, and a shared server that several people download models to.

Fine-tuning is the main reason to go large. Each saved checkpoint of a full model can be tens of gigabytes, and teams keep several to compare. See Can a GB10 Fine-Tune Your Own AI Model, or Is It Mostly for Inference?

Buy memory for the model you want to run. Buy storage for every model you will try along the way.

Does a faster SSD make AI faster?

Only when loading. Once a model is in the 128 GB of unified memory, the SSD is not involved in generating answers. A faster drive shortens the wait when you start a model or switch between models. As rough arithmetic using typical sequential read speeds, a 65 GB model loads in around ten seconds from a fast PCIe 4.0 drive and in roughly half that from a fast PCIe 5.0 drive; real-world times vary. If you switch models many times a day, or several users load different models, Gen5 is a nice-to-have. For one model left running, it makes little difference. On the GX10, only the 4 TB tier has a Gen5 drive; GIGABYTE offers a 4 TB PCIe 5.0 version for S$815 more than its 4 TB PCIe 4.0.

Can you upgrade later?

On most GB10 systems, yes. ServeTheHome found the GX10's SSD to be its only user-upgradable part, under the bottom plate, and Lenovo's PGX and HP's ZGX Nano drives were also reported as replaceable. Two cautions: the slot takes short M.2 2242 (or 2230) drives, not common 2280 drives, and replacing a drive means reinstalling DGX OS and your models. Check warranty terms with us before opening a unit. If your data must be encrypted at rest, note that NVIDIA documents the DGX Spark drive as self-encrypting and Dell's 4 TB option is a self-encrypting drive; ask about other brands.

Should you use network storage instead?

For archives, yes. Keep the models you use on the internal SSD and older models, datasets and checkpoints on a NAS over the 10 GbE port. For a cluster, ConnectX-7's 200 Gb/s links can also reach very fast storage, as covered in NVIDIA ConnectX-7 vs Ordinary Ethernet.

Which tier should you buy?

• 1 TB (GX10 1 TB, S$8,100) if budget is tight, you run a few models, or you will add a NAS.
• 2 TB (GX10 2 TB, S$8,300) for almost everyone else. S$200 more buys twice the space.
• 4 TB (GX10 4 TB, S$10,635, with a Gen5 drive; or DGX Spark 4 TB, S$8,800) for fine-tuning, large datasets or a shared model server.

Compute is identical across all tiers, so do not pay for storage expecting speed. For the brand comparison, see the GB10 buyer's guide and GX10 vs ZGX Nano vs Dell Pro Max GB10.

Buying in Singapore

SourceIT supplies GB10 systems in every listed storage tier from ASUS, NVIDIA, GIGABYTE, Lenovo, HP and Dell, with local warranty and a GST invoice. Browse the Ascent GX10 collection and the NVIDIA DGX Spark collection, or size your needs with the AI hardware calculator. For a quotation, use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.

Frequently asked questions

Does a 4 TB GB10 run AI faster than a 1 TB one? No. Compute and the 128 GB of memory are identical. Storage only affects how many models and datasets you can keep and, on some models, how quickly they load.

Is 1 TB enough for a DGX Spark or GX10? For one user with a few models, yes. Models are often stored in several formats and containers take space, so 2 TB is the safer default.

How big is GPT-OSS-120B on disk? About 59 GiB as a GGUF file and about 65.3 GB in its original MXFP4 format.

Can I upgrade the SSD in a GB10 system? On the ASUS GX10, Lenovo PGX and HP ZGX Nano, reviewers found the SSD replaceable. It takes an M.2 2242 or 2230 drive, not a 2280, and you will need to reinstall DGX OS.

What is the price difference between GX10 storage tiers? At SourceIT on 28 Oct 2026: S$8,100 for 1 TB, S$8,300 for 2 TB and S$10,635 for 4 TB.