AI PC · workstation
ASUS Ascent GX10 (NVIDIA GB10) review
A DGX Spark-class GB10 box with the full CUDA stack: 128GB and 1,000 TOPS, on Linux not Windows.
- Memory
- 128 GB
- Bandwidth
- 273 GB/s
- AI compute
- 1000 TOPS
- £ / GB
- £43
What it runs
With 128 GB of unified memory, leaving the system its share and room for context, you can load at 4-bit a model up to roughly
~141B params
Verdict
NVIDIA's GB10 platform in an ASUS chassis: 128GB and native CUDA on your desk, and with NVIDIA now listing the DGX Spark at $6,950 it's the cheaper way into the same hardware.
BEST FOR
- + developers who need CUDA locally
- + clustering two for 256GB
NOT FOR
- - fast single-stream tokens
- - anyone wanting Windows
The ASUS Ascent GX10 is Asus’s take on Nvidia’s little GB10 supercomputer - the same Grace-Blackwell chip as the DGX Spark, 128GB of unified memory, the full CUDA stack, and a ConnectX-7 NIC for lashing two together into a 256GB pair. One correction up front: it runs Linux, not Windows. This is a developer’s CUDA box, and with NVIDIA now asking $6,950 for its own Spark, it’s currently the cheaper door into the platform.
The take
Buy it for the software and the memory, not the raw speed - the same line as every GB10 box. 128GB lets you load models a 24GB card can’t hold, and you get native CUDA and the DGX software stack to do it on, which is the real draw for anyone who lives in Nvidia’s tooling. But the memory runs at around 273 GB/s, so single-stream token speed on a large model is a walk, not a sprint. The ConnectX-7 lets you pair two into one 256GB machine, though the DGX Spark carries the same NIC, so that’s no reason to pick one over the other. Price is: ASUS’s store lists the GX10 from around $5,599, while NVIDIA has the Spark at $6,950, so for now the GX10 is the cheaper route to the same chip. Just don’t expect Windows either; this is Ubuntu and the DGX stack.
What it’ll actually run
The 128GB is the point. A 70B at Q4 needs roughly 40GB, so it drops in with masses of room for context, and you can push into 120B-class models or run a 70B at higher precision than any single consumer card allows. Clip two GX10s together over the ConnectX-7 link and you’ve 256GB to play with, enough for really large models at home.
The catch is the same as the Spark: bandwidth. All that memory feeds the chip at about 273 GB/s, so tokens land at a steady walking pace on big models rather than instantly. The headline 1,000 AI TOPS of FP4 compute is huge, but local inference is limited by bandwidth, not maths, so it’s capacity and software that sell this box, not speed. And that software is the treat - native CUDA, the DGX stack, every framework already at home. Just remember it’s Linux only; if you were hoping to run Windows on it, this isn’t that machine.
Who should buy it
Buy it if you’re a developer or researcher who wants Nvidia’s stack and 128GB on your desk without renting cloud time, and Linux is no barrier. For that person the GX10’s case over the DGX Spark is price, not features - the two share the chip and the ConnectX-7 NIC, and at around $5,599 against NVIDIA’s $6,950 the ASUS is the cheaper of the pair right now.
Give it a miss if you wanted a Windows machine, or if single-stream speed matters more than capacity. A used 3090 runs anything that fits in 24GB far quicker, and a Strix Halo box loads the same 128GB for less if you don’t need CUDA. But for native Nvidia tooling, big models on your desk, and the option to cluster two into 256GB, the GX10 makes a good case - only on Linux, mind.
Starting settings
Starting configurations, each with where its figure comes from. A sensible starting point, not gospel - tune to your own kit.
Biggest model on one box
gpt-oss-120b MXFP4 (~60GB) or 70B Q4
Loads with room for context; MoE models keep single-stream speed up.
Go bigger
Cluster two GX10 over ConnectX-7 for 256GB
The pair holds models neither could alone; the ConnectX-7 link is what enables it.
Fast single-user chat
gpt-oss-20b MXFP4
The everyday sweet spot; smaller models feel quick.
Software
Ubuntu with the DGX stack, native CUDA, vLLM or llama.cpp
The whole Nvidia stack runs; it's Linux, not Windows.
Notes from people who've run one
Each note below names its source underneath.
- “
Reviewers pair two GX10 units over the ConnectX-7 link to build a 256GB desktop cluster, running models neither box could hold alone; single-box, it behaves like a DGX Spark - big capacity, modest single-stream speed.
- “
The GX10 uses the same GB10 chip and roughly 273 GB/s memory as the DGX Spark, so single-stream token speed on large models is a walking pace; compute (1,000 TOPS FP4) is not the limit, bandwidth is.
- “
It ships with Ubuntu Linux and Nvidia's DGX software, not Windows; the ConnectX-7 SmartNIC is aimed at clustering rather than everyday networking.
4 claim(s) we couldn't fully verify
- · memory_bandwidth_gbs 273 - ASUS doesn't list a bandwidth figure; 273 GB/s is the NVIDIA GB10 platform figure (LPDDR5x, 256-bit at 8533 MT/s), shared with the DGX Spark.
- · power_w 240 - ASUS lists a 180W EPR USB-C PD input, not a draw figure; 240W follows the GB10 platform's external-supply class (SoC TDP about 140W). Approximate.
- · Price around GBP 5,500 (USD 5,599 verified on the ASUS store 2026-11-12) - USD is the ASUS Store 'starting at' price. The GBP figure is from the Sept 2026 grounded sweep and was not re-checked against a UK maker store. The Sept framing (GX10 ~$5,999 above a ~$4,699 Spark, ConnectX-7 as its differentiator) was wrong on both counts by Nov 2026: the Spark also has ConnectX-7 and NVIDIA now lists it at $6,950. Body/verdict/FAQ corrected 2026-11-12.
- · The task brief's Windows framing - Corrected: ASUS's own tech specs list Ubuntu Linux; GB10 boxes run the DGX/Ubuntu stack, not Windows.
Sources we drew on
The spec sheets, vendor pages and reviews behind this page.
- ASUS Ascent GX10 - Tech Specs (vendor spec)ASUS · primary source
- DGX Spark Hardware Overview (vendor spec)NVIDIA · primary source
- NVIDIA Marketplace - DGX Spark (store price)NVIDIA · primary source
- ASUS Ascent GX10 Review: A new NVIDIA GB10 solutionServeTheHome · review
- I built a 256GB local AI cluster on my deskTechno Tim · review
Common questions
Does the ASUS Ascent GX10 run Windows?+
No. Despite the mini-PC looks, ASUS's own tech specs list Ubuntu Linux, and it runs Nvidia's DGX software stack. GB10 boxes are Linux machines, so if you were hoping to run Windows on it, this isn't that box.
How does the GX10 compare to the DGX Spark?+
They share the same GB10 Grace-Blackwell chip, the same 128GB of unified memory, and the same roughly 273 GB/s bandwidth. Both carry a ConnectX-7 NIC for clustering, so that isn't a difference either. What differs is the chassis, the storage and the price: ASUS fits a single M.2 slot (up to 4TB, PCIe 5.0) and lists the GX10 from around $5,599, while NVIDIA's own store has the 128GB DGX Spark at $6,950.
What size model can it run?+
A 70B at Q4 needs about 40GB, so it drops in with masses of room for context, and you can push into 120B-class models. Clip two GX10 units together over the ConnectX-7 link and you've 256GB to work with, enough for really large models at home.
Why isn't it fast despite 1,000 TOPS?+
Local inference is limited by memory bandwidth, not raw maths. The 1,000 AI TOPS of FP4 compute is huge, but at around 273 GB/s the memory feeds the chip slowly, so single-stream tokens land at a walking pace on large models.
