DGX Spark GB10 Grace Blackwell, 128GB vs Strix Halo Corsair AI Workstation 300, 128GB for local AI

Both hold 33 of the 39 open models here. The DGX Spark, 128GB costs $1 less. On Qwen3.8 27B, the strongest model both hold, the DGX Spark, 128GB is about 1.1× faster: 11 tok/s against 10, both estimated from memory bandwidth. Both pay for themselves in 62 years at 500k tokens a day.

The 133 W beside the Corsair AI Workstation 300, 128GB is a stand-in, not a figure for that machine: the data borrows it from the nearest hardware it does have, and the machine's own page names which and why. So the two figures above are not like for like, and the electricity in its pay-back here is priced from a borrowed number.

Run the numbers on the DGX Spark GB10 Grace Blackwell, 128GB · or the Strix Halo Corsair AI Workstation 300, 128GB

The same money, two different machines

These two cost within $1 of each other: $4,699 for the DGX Spark, 128GB and $4,700 for the Corsair AI Workstation 300, 128GB. NVIDIA makes one and Corsair the other. Every other head-to-head on this site holds a piece of the hardware equal and asks what the price gap buys. This one holds the price, so the row that usually carries the answer is the row the two machines agree on, and everything under it is what the same money buys twice.

How much use it takes to pay back

Everything above is at 500k tokens a day. Pay-back moves with how much you actually run, so here are both machines on Qwen3.8 27B, the strongest model both hold, at the five levels of use the calculator names. The Strix Halo Corsair AI Workstation 300, 128GB pays back sooner at every level up to 4M tokens a day. Above that the DGX Spark GB10 Grace Blackwell, 128GB does.

On Qwen3.8 27B neither machine can generate 20M tokens a day: the DGX Spark GB10 Grace Blackwell, 128GB manages at most 15.2M and the Strix Halo Corsair AI Workstation 300, 128GB at most 14.3M. Both figures on that row are for the most each can do.

Run the DGX Spark GB10 Grace Blackwell, 128GB at 20M tokens a day · or the Strix Halo Corsair AI Workstation 300, 128GB

The same models, not to the same length

Every model on this list that fits one machine fits the other at 32k of context, so memory does not change what they run. What it changes is how far you can take the context on 2 of them. The DGX Spark GB10 Grace Blackwell, 128GB has 119.5 GB usable against 96 GB, and spare memory is what the KV cache grows into as you keep more tokens.

Each figure is the longest context the calculator offers that the machine still holds that model at, and no model is taken past its own context limit. The other 37 models the calculator counts reach the same length on both machines, at every setting from 4k to 256k.

Run Ling 3.0 flash on the DGX Spark GB10 Grace Blackwell, 128GB at 256k · or on the Strix Halo Corsair AI Workstation 300, 128GB at 128k

The DGX Spark, 128GB is also head to head with another computer: Mac mini M5 Pro, 24GB · Mac Studio M5 Max, 128GB · GMKtec EVO-X2, 128GB · MacBook Air M5 (15-inch), 16GB · MacBook Pro M5 Pro (16-inch), 64GB. With a graphics card: RTX PRO 6000 Blackwell, 96GB · Radeon AI PRO R9700, 32GB.

The Corsair AI Workstation 300, 128GB is also head to head with another computer: Framework Desktop, 128GB. With the same box and the smaller chip: 64GB.

The assumptions behind both columns

Both columns use the same usage: 500k tokens a day at 15:1 input to output, 32k of context, $0.17 per kWh, and today's API prices held flat. Speeds marked estimated are worked out from memory bandwidth rather than measured, and pay-back scales with them. Change any of it in the calculator.

More head to head: everything the DGX Spark GB10 Grace Blackwell, 128GB runs · everything the Strix Halo Corsair AI Workstation 300, 128GB runs · every other match-up · the quickest pay-back at each level of use · every model against the frontier