The Debunked Myth: LLM Performance on Windows and Linux

For years, the tech community has often claimed a presumed superiority of Linux in terms of performance for intensive workloads, including Large Language Model (LLM) Inference. This perception has led many developers and infrastructure architects to favor Linux-based operating systems for on-premise AI solution Deployments. However, a recent test conducted on a consumer hardware configuration has challenged this belief, revealing substantial parity between Windows 11 and Ubuntu 26.04 when running Mixture of Experts (MoE) LLMs via the popular llama.cpp Framework.

The investigation aimed to verify the actual performance loss when switching from Linux to Windows for LLM Inference, a relevant question for those evaluating the flexibility and costs associated with local Deployments. The results suggest that, at least for medium and large MoE models, the choice of operating system might have a smaller impact than commonly believed, shifting the focus to other critical factors for resource optimization.

Test Configuration and Methodology

The setup used for the Benchmarks consisted of a robust workstation, designed to maximize local Inference capabilities. The hardware configuration included a Core Ultra 7 265KF CPU, liquid-cooled and slightly overclocked to 5.6/4.7 GHz, paired with 192 GB of Kingston Beast DDR5 RAM at 6400 MHz. The core of the system for Inference was an array of Nvidia GPUs: one GeForce RTX 5080 16 GB alongside two GeForce RTX 5060 Ti, also 16 GB each, all undervolted with a slight memory overclock to optimize efficiency. Power was managed by a 1200 W PSU, ensuring ample headroom.

On the software front, Ubuntu 26.04 (with KDE, GNOME, and Xfce desktop environments for an additional test) and Windows 11 were used, both with the latest updates installed. The llama.cpp version was identical across both systems, compiled with CMake and included a recent commit aimed at reducing VRAM usage through the use of f16 masks for attention. The models tested included Qwen 3.5 122B Q8, Qwen 3.5 397B iq4_xs, and MiniMax 2.7 Q5, all Quantization MoE models. The llama.cpp launch parameters were configured to optimize hybrid CPU+GPU offloading, with specific settings for context management and reasoning budget.

Analysis of Results and Deployment Implications

The Benchmarks revealed surprising performance parity between Windows 11 and Linux. For the Qwen 3.5 122B model, Windows recorded 300 Tokens/second for prompt processing (PP) and 28 Tokens/second for generation (TG), while Linux showed values of 290 PP and 28.5 TG. Similarly, with the Qwen 3.5 397B, Windows achieved 140 PP and 16 TG, compared to Linux's 150 PP and 15.2 TG. The MiniMax 2.7 also showed minimal differences. These data indicate that, for MoE LLM Inference with hybrid offloading, the choice of native operating system does not introduce a significant bottleneck.

A notable exception was execution via Windows Subsystem for Linux (WSL), which showed a considerable performance drop. With the Qwen 3.5 397B, WSL recorded only 110 PP and 13.5 TG, highlighting how the virtualized environment can introduce non-negligible overhead compared to native execution. This data is crucial for companies considering hybrid Deployments or seeking to leverage WSL for flexibility, suggesting that for intensive workloads, native execution remains the preferred path.

Considerations for On-Premise Deployments

These results offer important insights for CTOs, DevOps leads, and Infrastructure architects evaluating Deployment strategies for LLMs. The demonstrated parity between Windows and Linux for llama.cpp with large MoE models means that the operating system decision can be guided by other factors, such as team familiarity, existing software licenses, or corporate security policies, rather than a presumed intrinsic performance advantage.

Furthermore, the test highlighted that running complex models like the Qwen 3.5 397B does not require "massive" power consumption, peaking at 550-600 watts from the wall. This data is relevant for the Total Cost of Ownership (TCO) analysis of on-premise Deployments, debunking the idea that large-scale LLM Inference is inherently prohibitive in terms of energy consumption and space. For those evaluating Self-hosted Deployments, AI-RADAR offers analytical Frameworks on /llm-onpremise to assess the trade-offs between performance, TCO, and data sovereignty, supporting informed decisions without direct recommendations. The ability to reproduce these results with the provided parameters underscores the importance of transparency and independent verification in the field of AI.