Any takers for local llm? NVIDIA’s CUDA Moat Is Accelerating RTX Spark Laptop Sales, Higher 128GB Unified Memory Options Belonging To Surface Laptop Ultra Already Out Of Stock

# NVIDIA’s CUDA Moat Is Accelerating RTX Spark Laptop Sales, Higher 128GB Unified Memory Options Belonging To Surface Laptop Ultra Already Out Of Stock Source: [https://wccftech.com/nvidia-cuda-moat-rtx-spark-laptop-sales-surface-laptop-ultra-128gb-out-of-stock/](https://wccftech.com/nvidia-cuda-moat-rtx-spark-laptop-sales-surface-laptop-ultra-128gb-out-of-stock/) Microsoft’s product page for the[Surface Laptop Ultra](https://wccftech.com/microsoft-teases-rtx-spark-powered-surface-laptop-ultra-starting-at-2599/)shows the[RTX Spark](https://wccftech.com/nvidia-enters-pc-space-with-rtx-spark/), 128GB unified memory, and 1TB SSD configuration priced at $5,899\.99 is already listed as out of stock\. While the lower\-tier options are up for grabs, it indicates that the sellout may partly reflect limited early supply, but that’s unconfirmed because Microsoft hasn’t shared any data\. However, the demand behind these RTX Spark laptops makes sense when you sit down to contemplate that these machines are tailor\-made for a bigger user base, and a lot has to do with CUDA support\. ## The full NVIDIA software stack, tackling a multitude of workloads, is a prime factor why RTX Spark laptops are picking up in popularity With the unified memory architecture \(UMA\) paired with NVIDIA’S RTX platform and full CUDA support, developers and creators already tied into heavy workflows will find machines like the Surface Laptop Ultra incredibly useful\. Most AI frameworks, inference engines, and creative tools are written and optimized for CUDA first, so buyers can run their existing software stack without hunting for workarounds\. Sure, rival platforms like AMD’s[Ryzen AI MAX 400](https://wccftech.com/amd-pushes-ryzen-ai-max-400-to-192gb-memory-single-chip-run-300b-ai-llms-locally/)can offer large memory pools of up to 192GB, but few can match that software compatibility\. For professionals, time lost to tooling is more costly than spending a small fortune on premium hardware\. Previously, NVIDIA’s fastest consumer\-grade GPU shipped with 32GB of VRAM\. With the RTX Spark, that memory count has reached 128GB and is dynamically shared between CPU and GPU\. NVIDIA has claimed that it’s possible to run 120B AI models with up to a one\-million Context Window, making RTX Spark laptops incredibly attractive to developers, researchers, and companies handling sensitive data that want to run large models privately and avoid recurring cloud costs\. Since the top configuration of the Surface Laptop Ultra offers this unified memory headroom, it explains why it’s the one that sold out first\. [](https://cdn.wccftech.com/wp-content/uploads/2026/10/Surface-Laptop-Ultra-with-128GB-unified-memory-out-of-stock.jpg)That’s not to say that there aren’t any trade\-offs with RTX Spark laptops, with the most glaring one being memory bandwidth\. At just 273GB/s, the Surface Laptop Ultra pales in comparison with something like the M5 Max MacBook Pro, which, at the same 128GB unified RAM count, boasts up to a whopping 614GB/s bandwidth, which can become the difference in workloads dependent on increased throughput\. For example, the laptop version of the RTX 5090 handily beats the M5 Max in both prompt processing and token generation with up to a[133 percent performance increase in LLM benchmarks](https://wccftech.com/rtx-5090-laptop-vs-m5-max-llm-benchmarks/)thanks to its 896GB/s memory bandwidth\. Unfortunately, it falls short in running the denser models thanks to its 24GB GDDR7 VRAM limitation\. This is where the RTX Spark shines, with NVIDIA’s CUDA ecosystem locking in a plethora of users\. We’ve reached out to Microsoft and asked when the stock of the top\-end model will be replenished, so we’ll keep potential customers up to date on the latest\. [](https://wccftech.com/author/omarsohail/) **About the[author](https://wccftech.com/author/omarsohail/):**Omar Sohail is a reporter and analyst for Wccftech's[mobile](https://wccftech.com/topic/gadgets/)section, specializing in the technology and business of the mobile industry\. His expertise lies in the intricate hardware supply chain, covering developments in semiconductor manufacturing, chip lithography, and camera sensor technology\. Follow[Wccftech on Google](https://profile.google.com/cp/Cg0vZy8xMWM3NDB2MmIyGgA)to get more of our news coverage in your feeds\.