NVIDIA DGX Station GB300 vs Mac Studio M5 Ultra: What 100,000 Dollars Actually Buys

We already compared the far smaller DGX Spark against this same Mac Studio and found the Mac decisively faster on single stream decode while a networked Spark cluster won on concurrent throughput. The DGX Station GB300 is not the same comparison at a bigger scale. It is a different product tier entirely, roughly ten times the price of the top Mac Studio configuration, built to replace a small rack mounted GPU server rather than to compete with a developer's desktop. Putting a number on what that price difference actually buys is the useful exercise here, not a tokens per second leaderboard.

The spec gap is real, and NVIDIA shipped slightly under its own original numbers

The DGX Station GB300 pairs a 72 core Grace CPU with 496GB of LPDDR5X system memory and a Blackwell Ultra B300 GPU with 252GB of HBM3e, connected at 7.1 terabytes per second of memory bandwidth, according to ServeTheHome's hands on coverage of general availability. That bandwidth figure alone is nearly six times the Mac Studio M5 Ultra's 1.2 terabytes per second, and the memory capacity, 496GB versus a maximum of 256GB on the Mac Studio shipping today, opens up model sizes the Mac simply cannot load regardless of how patient you are willing to be about decode speed. Worth noting: NVIDIA's original 2025 spec called for a fully enabled B300 with 288GB of HBM3e and 8 terabytes per second of bandwidth. The shipping DGX Station appears to enable only seven of eight HBM3e stacks, landing about 12 percent under the originally announced ceiling. That is still a formidable machine. It is also a reminder to benchmark the unit you can actually buy rather than the spec sheet NVIDIA published a year earlier.

The price difference is not just a number, it changes what the machine is for

A Mac Studio M5 Ultra at 10,799 dollars is a purchase an individual engineer or a small team can make out of a normal hardware budget without a procurement cycle. A DGX Station GB300 at an estimated 100,000 to 125,000 dollars is a capital expenditure that requires the same kind of justification a small GPU server or a reserved cloud instance commitment would, and realistically competes against those alternatives rather than against a desktop Mac. The DGX Station also draws up to 1.6 kilowatts, which is the ceiling of a standard North American 120V 20A circuit, meaning it is not a drop in replacement for a desk setup the way a Mac Studio is. It needs a dedicated circuit, which in practice means it lives in a server room or a dedicated lab space, not next to a monitor.

Where the DGX Station actually wins on capability, not just speed

The honest case for the DGX Station GB300 is not that it beats a Mac Studio on tokens per second for a model that fits comfortably in 256GB of memory, it is that 496GB of system memory and 252GB of dedicated GPU memory open up workloads that are simply out of reach on Apple Silicon today: larger dense models run at full precision, multi model serving where several large models need to stay resident simultaneously, and fine tuning or light training workloads where the Mac Studio's unified memory architecture, optimized for inference, is not the right tool regardless of capacity. If your actual workload is bounded by whether a model fits in memory at all, rather than by how fast a model you have already fit decodes, the DGX Station's memory ceiling is the number that matters, and no amount of Mac Studio memory bandwidth closes that gap because the Mac simply cannot load the model in the first place.

Where a Mac Studio, or several, still wins on practical economics

For the overwhelming majority of local inference workloads at a software team's actual scale, a single developer or a single agent pipeline running a model that fits in 256GB, the Mac Studio's price to performance is not close, even before counting the DGX Station's power and cooling requirements. You could buy roughly nine fully configured Mac Studio M5 Ultra units for the price of one DGX Station GB300, and nine independent machines serving nine independent workloads is a more useful allocation of that budget for most engineering organizations than one very large machine serving one workload at a time. The efficiency side of that math, what the extra bandwidth and memory actually cost in power over a machine's working life, is the same calculation we walked through across the M5 lineup's tokens per watt figures, and the DGX Station's 1.6 kilowatt ceiling versus a Mac Studio's standard desktop draw is not a small difference once you are running either machine continuously.

The decision in practice

Buy a DGX Station GB300 if your actual constraint is memory capacity for a single very large model, a multi model serving setup where several large models need simultaneous residency, or a light training and fine tuning workload where CUDA's ecosystem maturity matters more than raw inference throughput per dollar, and you have the facilities, a dedicated circuit and a space that is not someone's desk, to run it. Buy one or several Mac Studio M5 Ultra units if your workload is inference bound, fits within 256GB per model, and your actual constraint is serving individual developers or agent pipelines rather than one enormous shared model. For nearly every team we work with, that second description matches their actual workload, and the DGX Station's capability ceiling is not one they are currently pressing against, which makes it an expensive answer to a question most teams are not yet asking.

FAQ

Is the DGX Station GB300 the same product as the DGX Spark? No. The DGX Spark is a much smaller, roughly 4,000 dollar unified memory box designed to be networked in small clusters. The DGX Station GB300 is a full workstation tier product at roughly 25 to 30 times the Spark's price, built around a far larger Grace CPU and Blackwell Ultra GPU configuration.

Why did NVIDIA ship less memory and bandwidth than originally announced? NVIDIA has not published a detailed explanation. ServeTheHome's coverage notes the shipping configuration enables seven of the eight available HBM3e stacks on the B300 GPU, landing about 12 percent under the original 2025 specification, which is the kind of gap worth confirming directly against current spec sheets before budgeting around the originally announced numbers.

Can I run a DGX Station GB300 in a normal office without special preparation? Plan for a dedicated 120V 20A circuit given the system's 1.6 kilowatt power ceiling, which is beyond what a typical office outlet or power strip is rated for. Most buyers are placing these in a server room or dedicated lab space rather than at a desk.

Is there a meaningful middle option between a Mac Studio and a DGX Station? NVIDIA's own DGX Spark and a Spark cluster sit in that middle tier price wise, and Supermicro's GB200 based Super HPC Station is a comparable capacity alternative aimed more at HPC workloads than at AI inference specifically.

When this applies to your stack

If your team's local inference workload is bounded by whether a model fits in memory rather than by how fast it decodes once loaded, and you have the facilities to run a machine that needs its own circuit, the DGX Station GB300's memory ceiling is a real capability a Mac Studio cannot match regardless of price. If your actual workload is one developer or one agent pipeline running a model that comfortably fits in 256GB, the math overwhelmingly favors one or several Mac Studio units over a single very expensive machine sized for a problem you likely do not have yet. Contra Collective helps engineering teams size local and hybrid inference infrastructure against the workload they actually have, not the one the biggest available machine is built for.