GB10 vs GB300: When Is It Time to Move From a Personal AI Computer to an Enterprise AI Station?

Move from a GB10 to a GB300 when the machine stops being a personal tool and becomes a shared service. In practice that means one of four things: several people are waiting on the same endpoint, the model you need no longer fits in 128 GB, you need full-precision weights for fine-tuning or evaluation, or IT has to manage the box like a server. Until then, a GB10 system such as the NVIDIA DGX Spark (S$8,800) or ASUS Ascent GX10 (from S$8,100) is the better buy. GB300 towers such as the HP ZGX Fury G1n are price on request and need a planned installation. Prices at SourceIT, listed 28 Oct 2026.

What actually changes between GB10 and GB300?

Figures from NVIDIA's DGX Spark and DGX Station specifications. OEM units carry the same superchip and memory; chassis, cooling and support differ by vendor.

Signal 1: are people queuing for the same endpoint?

A GB10 serves one developer very well and a small team adequately. The limit is not the model but the queue. Every user's tokens come out of the same 273 GB/s memory system, so when five people send long prompts at once, everyone waits. The GB300's extra compute shows up exactly here: prompt processing on long documents and batching many requests together. In StorageReview's September 2026 review of the ASUS ET900N G3, the GB300 tower served GPT-OSS-120B at about 9,300 output tokens per second at full concurrency, which the reviewer put at roughly 18 times a DGX Spark on the same workload.

If your GB10 is mostly idle and occasionally busy, keep it. If staff have started scheduling around it, or you are building an internal assistant for a whole department, you have outgrown it. Our post Can One GB10 Support 20 Employees Using AI at the Same Time? works through the arithmetic.

Signal 2: has the model outgrown 128 GB?

NVIDIA rates one GB10 at up to about 200 billion parameters, and that figure assumes 4-bit weights. Two linked units pool 256 GB for 405B-class models. Beyond that, clustering more GB10s works but gets slower and more fragile with every node, as we explain in Four GB10 Supercomputers on One Desk. A GB300 holds a 400B-class model at FP4 entirely in its 252 GB of HBM3e and reaches NVIDIA's 1-trillion-parameter rating across the full 748 GB.

The question to ask is not "what is the biggest model we could run?" but "which model does the business actually need?" Many teams find a well-chosen 70B or 120B model does the job. If yours genuinely needs a 400B to 1T model on one system, that is a GB300 decision.

Signal 3: do you need full precision?

On a GB10, a 70B model must run at FP8 or FP4 because FP16 weights alone are about 140 GB. That is fine for chat and retrieval. It is a constraint for fine-tuning, for evaluating a model at the precision it will be deployed at, and for regulated work where quantisation needs justifying. A GB300 runs a 70B model at FP16 inside HBM3e with room left for the KV cache, and a 120B model at FP8. If your team keeps writing "results may differ at full precision" in its reports, the gap is costing you more than the hardware.

Buy a GB10 for people. Buy a GB300 for a service.

Signal 4: does IT need to run it like a server?

A GB10 is a desktop. It has no BMC, so there is no out-of-band console, and each box is its own operating system image. That is perfect for a developer's desk and awkward for an IT team that must patch, monitor and audit a shared AI service. The DGX Station class adds a BMC with Redfish for remote management and MIG to split one GPU into up to seven isolated instances, so different teams can share one system without seeing each other's work. If your security or audit team is asking who can access the AI box, where the logs go and how it is patched, that is a sign the workload belongs on managed infrastructure.

What does the move cost beyond the hardware?

• Power. NVIDIA rates the DGX Station at 1,600 W, so it needs its own circuit confirmed by an electrician. See our DGX Station office installation checklist.
• Space and handling. The ASUS ET900N G3 is a 27 kg liquid-cooled tower, not a 1.2 kg box.
• UPS and cooling. A 1,600 W load changes UPS sizing and the room's heat load.
• Administration. Someone owns user access, model updates and monitoring.
• Allocation lead time. GB300 systems are allocated and built to order, so plan the purchase in a budget cycle rather than on a credit card.

The running costs of any AI workstation are set out in The Hidden Costs of Owning an AI Workstation.

Does your GB10 work carry over?

Yes, and this is the strongest argument for starting on GB10. Both platforms are Grace Blackwell systems running NVIDIA's Linux AI stack, so containers, CUDA code, vLLM or NIM deployments and agent configurations built on a DGX Spark move up without a rewrite. The GB10s do not become scrap either. The common pattern we recommend is GB10s on developer desks for experiments and a single GB300 as the shared endpoint they deploy to.

Which GB300 system?

SourceIT lists five DGX Station GB300 towers, all price on request: the HP ZGX Fury G1n, the ASUS ExpertCenter Pro ET900N G3, the MSI XpertStation WS300, the GIGABYTE W775-V10-L01 and the Supermicro ARS-511GD-NB-LCC. For HP-standardised offices, HP ZGX Nano vs ZGX Fury compares the two HP tiers directly, and the DGX Station GB300 buyer's guide compares all five.

If you are not there yet, the DGX Station GB300 vs DGX Spark GB10 comparison covers the first purchase decision, and the GB10 buyer's guide ranks the GB10 systems.

Buying in Singapore

SourceIT supplies GB10 and GB300 systems from NVIDIA, ASUS, HP, Dell, Lenovo, MSI, GIGABYTE and Supermicro with local warranty and a GST invoice, and can carry out a pre-delivery site check for a GB300 placement. Browse the AI workstations and servers collection, the NVIDIA DGX Spark collection or the HP ZGX AI Stations page. 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. Government buyers can purchase through GeBIZ.

Frequently asked questions

When should I upgrade from a DGX Spark to a DGX Station? When several users share one endpoint, when the model needs more than 128 GB, when you need full-precision weights, or when IT must manage the system with a BMC and isolate users. For one developer working interactively, a GB10 system is enough.

How much faster is GB300 than GB10? NVIDIA rates GB300 at 20 PFLOPS FP4 against 1 PFLOP for GB10. StorageReview measured an ASUS ET900N G3 at about 18 times a DGX Spark when serving GPT-OSS-120B at full concurrency; a single user sees a smaller gain.

Will my DGX Spark code run on a DGX Station? Generally yes. Both are Grace Blackwell systems on NVIDIA's Linux AI stack, so containers, CUDA code and serving setups move across without a rewrite.

How much does an HP ZGX Fury G1n cost in Singapore? It is price on request at SourceIT, quoted per configuration. GB10 systems such as the DGX Spark (S$8,800) and ASUS Ascent GX10 (from S$8,100) have listed prices.

What does a DGX Station need in the office? NVIDIA rates total system power at 1,600 W, so it needs a dedicated circuit, space for a large liquid-cooled tower, and UPS planning. A GB10 system runs from a 240 W adapter on any desk.