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Josephine Pfeiffer reports that Arch Linux ARM boots and runs on the 128 GB ASUS ProArt P16 H7607BA-SE033W, a laptop with NVIDIA RTX graphics and an ARM-based N1X processor. Her setup makes 116.0 GiB available to both the operating system and CUDA, and her published 7-Zip comparisons are broadly similar to Windows results, with test differences limiting direct conclusions.
Josephine Pfeiffer has published a bootable Arch Linux ARM setup for the 128 GB ASUS ProArt P16 H7607BA-SE033W, a Windows-supported laptop with NVIDIA RTX graphics and a 20-core N1X processor. Her report says Linux exposes 116.0 GiB of the system’s shared physical memory to both the operating system and CUDA, and describes running a large language model locally on the machine.
Pfeiffer, an Arch Linux package maintainer, built the installation media and hardware patches for the ASUS ProArt P16 H7607BA-SE033W. The configuration she tested has 128 GB of memory, 6,144 CUDA cores and a 2 TB SSD. Her work uses Arch Linux ARM, a separate project that ports Arch packages to ARM processors, rather than the standard x86 Arch distribution.
Getting the laptop to boot required changes for its hardware, according to Pfeiffer. She incorporated NVIDIA kernel work aimed at Ubuntu, adjusted ACPI handling and framebuffer mapping to bring up a console, and added support for the keyboard and audio. She also extended Archinstall 4.5 to install the required components. Her repository includes build scripts and patches; she says the packages are hash-pinned and that her hardware packages are signed with her Arch package maintainer key.
In CPU testing run October 19 with the laptop plugged in and set to performance mode, Pfeiffer compared native ARM64 7-Zip results with published Windows tests. Her 20-thread results were about 5% higher in compression and 3% higher in decompression than results for an HP OmniBook Ultra 16 reported by StorageReview. The single-thread results were nearly even. She cautions that the review did not specify its 7-Zip version or dictionary size, so the figures compare the tested laptop setups, not controlled software runs.
The main practical difference Pfeiffer reports is not a broad claim that Linux is faster, but how the laptop’s memory is exposed. Her Linux installation reports 116.0 GiB as system RAM and as CUDA memory from the same physical pool. In her account, that lets CPU and GPU workloads draw on the large pool without changing a GPU reservation and rebooting between uses.
That matters for people experimenting with local AI models, whose weights and cache can require tens of gigabytes. Pfeiffer says she loaded NVIDIA’s Qwen3.8-Flash-Next-FP4-Local checkpoint with vLLM in a native ARM64 container, using B12X CUDA kernels, mixed quantization and an FP8 key-value cache. The model used 68.2 GiB for loading and another 21.1 GiB for the cache, according to vLLM logs she cited. This is one developer’s reported configuration, not an independent compatibility test or a guarantee that every model will run the same way.
The project also offers a workaround for users who prefer Linux for privacy, customization or development reasons but are considering hardware whose official support is Windows-focused. It does not establish that every feature works, or that the installation is as straightforward or supported as a vendor-provided operating system.
Linux compatible ASUS ProArt P16 laptop
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Windows Support and Memory Reporting
The ProArt P16 configuration in Pfeiffer’s report uses an N1X ARM processor and NVIDIA graphics. She says official support for the laptop was Windows-only, which led her to adapt Linux support rather than wait for a vendor-provided distribution. Her starting point included NVIDIA kernel work for Ubuntu and the laptop’s own ACPI tables.
The memory figures need context. Pfeiffer says Windows divides physical memory into dedicated GPU, shared and CPU-only regions. A dedicated GPU reservation is not available to CPU workloads, while shared memory can extend CUDA’s reach under Windows’ memory budget. She cites a Frandroid test that reported 96 GiB of dedicated memory on a 128 GB ProArt P16; with a 91 GiB reservation, CUDA reported 109.7 GiB including shared memory. Her Linux figure of 116.0 GiB is about 6.3 GiB higher than that reported CUDA total, but it comes from a separate setup and is not a controlled head-to-head test.
For another Windows comparison, ComputerBase tested a 64 GB ProArt P16 with the same 20-core N1X and reported a converted 7-Zip score within Pfeiffer’s three-run range. The systems differed in memory configuration and 7-Zip version, and the published chart did not specify its dictionary size. Those limits make the comparison useful as an initial reference, not a definitive operating-system benchmark.
“My Linux run reports 116.0 GiB as both system RAM and CUDA’s total, from the same physical pool.”
— Josephine Pfeiffer
NVIDIA RTX graphics laptop with Linux support
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Compatibility and Benchmark Limits
The report documents one specific laptop model and firmware version, H7607BA.300. It does not establish whether the patches work across other ProArt P16 configurations, firmware releases or similarly named RTX Spark systems. Pfeiffer’s repository provides installation materials, but the source does not describe vendor certification or ongoing manufacturer support.
The performance comparisons also have limits. StorageReview did not specify its 7-Zip version or dictionary size, while ComputerBase used version 26.00 and tested a 64 GB model. Those differences, along with testing on separate machines, mean the reported percentages should not be read as a controlled measurement of Linux versus Windows on identical hardware. The supplied report also ends before giving the Qwen request speed results, so throughput and response latency cannot be assessed from the information provided.
It remains unclear how the setup behaves in longer-term daily use, including reliability across updates, battery life, suspend and resume, and the full range of laptop functions. Pfeiffer describes holding kernel, NVIDIA, power-management and firmware packages while allowing other Arch Linux ARM packages to update; that is her maintenance approach, not evidence that future updates will be seamless.
ARM-based laptop with large memory pool
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Testing Beyond the Initial Setup
The next step for readers interested in reproducing the result is to review Pfeiffer’s published scripts and patches, which she says include instructions for extracting the laptop’s own ACPI tables and speaker firmware locally. The account does not announce a manufacturer-supported Linux release or a formal validation program.
Further testing could clarify compatibility across firmware versions and provide more controlled Linux-versus-Windows results on the same hardware, with matching 7-Zip versions, settings and power profiles. For the AI workload, the missing request-speed results and details about sustained performance would help show how useful the setup is beyond loading a model. Until then, the report stands as a documented community-built installation and an early account of its memory and CPU behavior.
Laptop for local AI model training
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Key Questions
Which laptop did Pfeiffer install Linux on?
She tested the ASUS ProArt P16 H7607BA-SE033W with 128 GB of memory, 6,144 CUDA cores and a 2 TB SSD. She reports firmware version H7607BA.300.
Which Linux distribution is used?
The setup uses Arch Linux ARM, a separate project from standard Arch Linux that ports packages to ARM hardware. Pfeiffer built bootable installation media and added laptop-specific support.
Does the report show Linux is faster than Windows?
No broad conclusion is established. In one comparison, Linux was about 5% ahead in 20-thread compression and 3% ahead in decompression, while single-thread results were nearly tied. The Windows tests were on separate laptop setups and did not provide all matching test details.
What does the 116.0 GiB memory figure mean?
Pfeiffer says Linux reports 116.0 GiB as both system RAM and CUDA memory from the same physical pool. It is her measurement on this configuration; Windows reports memory across dedicated, shared and CPU-only categories.
Did the setup run an AI model?
Yes. Pfeiffer reports loading NVIDIA’s Qwen3.8-Flash-Next-FP4-Local checkpoint through vLLM in a native ARM64 container. Her account gives 68.2 GiB for model loading and 21.1 GiB for the KV cache, but the supplied material does not include request-speed results.
Source: hn
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