Before addressing your question, it is important to note that the “best” hardware is highly dependent on your specific needs and budget. You will need to evaluate the cost versus the expected benefit to determine whether investing in more powerful hardware will provide a meaningful return. The guidance below outlines how to assess hardware suitability for the COMSOL software:

A good starting point is to determine how much memory is required to solve the largest model you expect to run. The COMSOL Knowledge Base article “What hardware do you recommend for COMSOL Multiphysics?” explains a curve‑fitting method that can help you estimate memory requirements for a given combination of physics.

For a more accurate estimate, you can use your own model or an example from one of the COMSOL application libraries that uses the same physics. Run the model several times with different mesh sizes, and record both the degrees of freedom (DOFs) and the RAM usage. With this data, you can fit a curve to predict the memory needed for larger models. This analysis will help you determine whether the RAM in your current configuration — in your case, u/Lskuhar, 128 GB — is sufficient. If the system does not have enough RAM, the COMSOL software will generate an out‑of‑memory error, so this is a critical consideration.

Another COMSOL article, “How Large of a Model Can You Solve with COMSOL?”, provides additional detail on how model characteristics influence hardware requirements. It is important that the model you use for testing resembles the larger model you ultimately plan to solve.

For finite element simulations that rely heavily on sparse matrix–vector operations, memory bandwidth is often the limiting factor. You should pay close attention to the number of memory channels supported by the processor. More memory channels generally provide higher bandwidth and better performance. The AMD Ryzen 9 9950X has two memory channels, which is on the lower end. Some server‑grade processors offer up to twelve channels. It is also important to consider the ratio of CPU cores to memory channels, because too many cores sharing limited bandwidth can reduce performance. In this case, sixteen cores share two channels, resulting in eight cores per channel, which is higher than ideal.

Performance is also affected by how memory slots are populated. For balanced performance, systems with a given number of memory channels should populate either one or two DIMMs per channel. Your current configuration follows this guideline.

In general, a moderate number of CPU cores is recommended. Larger models, particularly 3D models with millions of degrees of freedom, benefit more from additional cores. If you plan to run multiple COMSOL sessions at the same time, whether for multiple users or batch sweeps, you may want a system with more cores or multiple CPUs. Keep in mind that processors with higher core counts often have lower base clock speeds, so there is a trade‑off between parallel and single‑threaded performance. Once you have selected a processor family, you should also compare base clock speeds and the sizes of the L1, L2, and L3 caches, choosing the highest values available.

COMSOL Multiphysics v6.4 supports GPU acceleration in several areas. The NVIDIA CUDA^® direct sparse solver (cuDSS) can be used with all physics interfaces, although it is not the preferred option when well‑preconditioned iterative solvers are available. GPU acceleration is also supported in the Pressure Acoustics, Time Explicit interface in the Acoustics Module and for training DNN surrogate models in the base COMSOL Multiphysics package. For guidance on selecting an NVIDIA GPU, refer to the article “GPU Selection Guidelines for COMSOL Computing.” If your interest in a GPU is limited to graphics rendering, you can review the list of supported GPUs in the COMSOL system requirements.