For most mid-size .NET or C++ solutions in Visual Studio 2022, 12 cores is a practical and well-matched choice. MSBuild distributes compilation across all available threads, so the return is proportional to core count for projects with multiple translation units or projects in a solution. For a single small project compiling in under a minute anyway, the difference between 8 and 12 cores is modest. For a large solution with 50 or more projects, the Basalt's 12 cores will complete a full rebuild meaningfully faster than an 8-core machine and within a comparable range of a 16-core machine at a lower price point. If your solution is large and rebuild times are a daily frustration, call Kevin and tell him the project count and typical rebuild time on your current machine.

Yes, for a standard development environment. Running Docker Desktop with three to five containers, a WSL2 Linux session, a JetBrains IDE or Visual Studio actively indexing, and a browser with developer tools open typically requires 20 to 28GB under normal conditions. 32GB keeps all of that in memory with headroom. The constraint starts when the container count climbs above five or six, when you add a local database alongside the containers, or when you run a local Kubernetes cluster. At that point, the system begins paging and the slowdowns are intermittent and unpredictable. If your environment regularly runs more than six containers simultaneously, the G6 Graphite with 64GB is the right build.

The Basalt uses a Ryzen 9 9900X with 12 cores, 32GB DDR5, an RTX 5060 Ti 8GB, and a 2TB NVMe. The G6 Graphite uses a Ryzen 9 9950X with 16 cores, 64GB DDR5, an RTX 5060 Ti 16GB, and a 2TB NVMe. The practical difference is in environment scale: the Basalt handles a standard development environment of up to five or six containers and datasets up to 5GB on disk; the Graphite handles ten or more containers simultaneously, large monorepos with hundreds of translation units, and pandas DataFrames in the 5 to 15GB range on disk. The Graphite also supports entry AI and ML work — QLoRA 4-bit fine-tuning of 7B parameter models — via the 16GB VRAM on the RTX 5060 Ti. If you are not sure which one fits your current setup, call Kevin and describe your environment.

Yes, for datasets up to around 5GB on disk. pandas DataFrames at this scale load in full without memory pressure. Standard analytical workflows — groupby, merge, apply, scikit-learn model training with cross-validation — run without issues on 32GB. The Ryzen 9's 12 cores also benefit scikit-learn's parallel cross-validation via the n_jobs parameter. Where 32GB becomes the constraint is in datasets above 15GB on disk, multi-notebook sessions where multiple kernels hold large DataFrames simultaneously, or ETL pipelines joining multiple large datasets in memory at once. For those workloads, the G6 Graphite with 64GB is the appropriate build. The Basalt also works as a development machine with light data science work alongside software development — many developers use pandas and scikit-learn at dataset scales that fit comfortably in 32GB.

Build time is 3 to 5 working days from order confirmation, including the 24-hour stress test. Delivery to UK mainland addresses is free and fully tracked. If you have a specific date in mind, call Kevin on 01902 714533 before you order and he will tell you whether the timeline is achievable. He will also confirm whether Windows 11 Home meets your needs or whether a Pro upgrade should be arranged before dispatch.